A Multisensor Integration-Based Complementary Tool for Monitoring ...

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A Multisensor Integration-Based Complementary Tool for Monitoring Recovery Progress of Anterior Cruciate Ligament-Reconstructed Subjects Owais A. Malik, S. M. N. Arosha Senanayake, Senior Member, IEEE, and Danish Zaheer

Abstract—Anterior cruciate ligament (ACL) trauma, being one of the most common musculoskeletal injuries in sports, leads to knee joint instability and causes ambulation impairments. A careful monitoring of the progress of recovery after ACL reconstruction is crucial for minimizing postoperative complications and reinjuries. This research is aimed at designing a complementary tool to assess the recovery status and knee dynamics during the rehabilitation period after ACL reconstruction. The prototype includes wireless body-mounted motion sensors for kinematics measurements, surface electromyography system for muscle activity measurements, a video camera for recording trial activities and custom-developed intelligent system software that provides classification of the progress of the recovery and visual biofeedback during rehabilitation. The subjects’ recovery stages are classified based on combined features from sensors’ data, using an adaptive neuro-fuzzy inference system. The visual biofeedback provides monitoring of different signals simultaneously in order to help in detecting the intra and intersubject variability and correlation between the knee joint dynamics and muscle activities. The promising results of this initial study for assessing the ambulation at various speeds showcase the prospects of using the proposed system as part of existing rehabilitation monitoring procedures to achieve a more effective and timely recovery of ACL-reconstructed subjects. Index Terms—Electromyography, gait analysis, knee injury, motion sensors, multisensor integration, recovery monitoring.

I. INTRODUCTION HE knee joint plays a fundamental role in lower limb activities together with coordinated movements of the articulating bones, surrounding muscles and ligaments. Injury to either of these structures results in altered locomotory patterns and joint instability. One of the most common knee injuries is tearing of the anterior cruciate ligament (ACL) which has various short- and long-term impacts on injured patients including dynamic knee joint instability, neuromuscular impairments, loss of proprioception, cartilage degeneration and the early onset of osteoarthritis [1], [2]. In order to restore the knee kinematics

T

Manuscript received September 6, 2014; accepted November 19, 2014. Date of publication December 24, 2014; date of current version August 24, 2015. Recommended by Technical Editor S. Q. Xie. This work was supported by the University Research Council Grant Scheme at the Universiti Brunei Darussalam under Grant UBD/PNC2/2/RG/1(195) with the title “Integrated Motion Analysis System.” O. A. Malik and S. M. N. A. Senanayake are with the Universiti Brunei Darussalam, Brunei-Muara BE1410, Brunei Darussalam (e-mail: [email protected]; [email protected]). D. Zaheer is with the Sports Medicine and Research Center, Hassan Bolkiah National Stadium, Berakas BB4313, Brunei Darussalam (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TMECH.2014.2376199

and kinetics and repair neurophysiologic dysfunction, ACL reconstruction is generally recommended. However, there is some contradictory evidence about the effectiveness of ACL reconstruction in preventing cartilage degeneration, osteoarthritis and reinjuries which could suggest incomplete restoration of normal kinematics and neuromuscular function following ACL surgery [1], [3], [4]. Reduced knee flexion angles/moments at midstance, an extension deficit during the swing phase, gait variability and reduced strength and activity of muscles surrounding the knee joint have been reported to persist in ACL-reconstructed (ACLR) subjects for several months after surgery [4]–[7]. It has been estimated that approximately 20–25% of athletes will suffer reinjury after ACL reconstruction and the chances of a second injury in the same or contralateral leg are higher than in healthy subjects [8], [9]. Moreover, it has been reported that around two-thirds of the athletes were not able to regain their preinjury level of sports activity within one year after surgery [10]. These findings suggest that a careful monitoring of the recovery process after ACL surgery is crucial in reestablishing knee joint and neuromuscular control, and in avoiding or minimizing postoperative complications and reinjuries. In current clinical settings, ACL-R subjects perform rehabilitation exercises and training based on a prescribed rehabilitation protocol. Often, the subjects are allowed to return to sports activity six months after surgery but many studies advocate the use of objective criteria as opposed to mere time-based evaluation of the subjects [11]. Generally, rehabilitation protocols include certain prescribed tests and the evaluation of the recovery progress is either subjective or partially objective or based on a limited set of parameters collected using traditional measuring equipment (e.g., an arthrometer/goniometer) which can be obstructive and cumbersome [12], [13]. Additional recovery assessment tools and methods may be designed to assist clinicians and subjects by providing a more objective evaluation through real-time or offline biofeedback [14], [15]. Kinematics and surface electromyography (EMG) signals have been found effective in providing quantifiable complementary biofeedback to physiatrists/clinicians and subjects during post ACL surgery recovery period [16]–[18]. In most of the previous studies, optical motion capture and hard-wired EMG systems have been used to capture the human motion parameters [4], [7], [19], [20]. Optical motion capture systems provide reliable and accurate information about human motion but these are usually expensive, sensitive to calibration and reflections, bulky and take a longer setup time for experiments [21]. Microelectromechanical systems (MEMS) and other wireless sensors have been

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MALIK et al.: MULTISENSOR INTEGRATION-BASED COMPLEMENTARY TOOL FOR MONITORING RECOVERY PROGRESS

Fig. 1.

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Input/output of the assistive recovery monitoring system with sensors and information flow.

demonstrated as a viable alternative mechanism for recording human motion data and developing portable rehabilitation monitoring systems, generally using one type of feature (either kinematics or kinetics or muscle activity) [21]–[23]. In contrary, multisensor data integration and fusion provide more comprehensive information than single-source data with wider usage and applicability in medical applications [24]–[26]. This study investigates the development of an intelligent complementary tool for classifying the recovery status and providing visual analyses to assess the progress of post ACL reconstruction rehabilitation. Novel mechanisms based on kinematics and muscle activity and portable, reconfigurable and wearable technologies have been introduced to develop a more comprehensive and objective evaluation of the subjects’ recuperation. The proposed system is mainly composed of two components: 1) measurement units consisting of wireless motion (gyroscopes and accelerometers) and EMG sensors and 2) a software module that processes the data and displays the results. The knee kinematics and neuromuscular data are acquired simultaneously through wireless wearable motion and EMG sensors, respectively, at various walking speeds. These signals are integrated to generate a pattern set of features that has been used to design an adaptive neuro-fuzzy inference system (ANFIS) which intelligently classifies the recovery progress of ACL-R subjects. Visual biofeedback is also provided by displaying individual and multiple superimposed signals simultaneously to identify the variations in ambulation patterns for healthy and ACL-R subjects at different stages of recovery. The system provides postprocessed offline feedback which can be used as supporting information by clinicians, physiatrists and physiotherapists for observing the subjects and adjusting their rehabilitation protocols as required and focusing on specific recovery problem areas. Our intentions in designing this recovery assessment tool are to help in reducing the duration and cost of recovery, and to improve the rehabilitation process by providing accurate and timely information about the subjects’ knee functionality. The idea of incorporating ANFIS and visual biofeedback for ACL recovery assessment, based on integrated features extracted through wireless bio-signals with reconfigurable software has so far not been extensively explored or addressed in the literature.

II. SYSTEM HARDWARE The kinematics and EMG data from the knee joint and related muscles were collected using two major hardware components: 1) four microelectromechanical motion sensor units and 2) a surface EMG monitoring unit with electrodes (see Fig. 1). The details of the hardware components are explained below. A. Wireless MEMS Sensors The kinematics data (angular rate and linear acceleration) were collected using KinetiSense MEMS motion sensors for measuring the motion of the subjects’ lower extremities. The KinetiSense (ClevMed. Inc.) is a bio-kinetic analysis system consisting of a command module, a wireless transmission radio and sensor units (see Fig. 1). Each sensor unit (size: 2.2 cm × 1.5 cm × 1.25 cm) contains a triaxial MEMS accelerometer and a triaxial MEMS gyroscope to measure 3-D linear accelerations and 3-D angular velocities, respectively. The small and light-weight wearable sensors do not obstruct human motion. The data recorded (at a sampling rate of 128 Hz) by the sensors are wirelessly transferred through a USB receiver to the computer where the KinetiSense software records the readings for each experiment. B. Wireless EMG Capturing System A biocapture physiological monitoring system, consisting of a bioradio and USB receiver, was used to record the EMG signals from knee extensors and flexors (see Fig. 1). The bioradio, records the EMG signals through surface electrodes, does initial processing (amplification, sampling and digitization) of the data and then wirelessly transmits them to the computer using a USB receiver. The bioradio recorded the data at a sampling rate of 960 Hz at 12-b analog/digital conversion. For surface EMG signal analysis, a frequency range between 5–450 Hz has been found adequate as most of the signal power lies within this range [27]. In addition, the 2-D linear acceleration was also recorded to mark the gait cycle (see Section IV). The EMG was recorded using Kendall disposable pregelled snap electrodes (Ag/AgCl sensor, 24 mm). A video camera (Sony HDR-XR520, 50 frames/s) was also used to record the walking trials in order to monitor the

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Fig. 3. Placement of motion and EMG sensors on the lower extremities of the body with axes alignments. (a) Front view. (b) Back view.

for further processing or output generation. These system outputs and other standard tests are then used by the physiatrists, physiotherapists and trainers for suggesting and/or modifying the training and exercises during the next phase of rehabilitation. The subject is reassessed after further rehabilitation training and the improvements or deteriorations are objectively identified. This process continues till a subject fulfils the required criteria of recovery evaluation before joining any high level sports or other demanding activities. The details of the component-based system software are described in the following sections. A. Sensors’ Placements and Data Acquisition

Fig. 2. Components of the hardware/software codesign for monitoring the recovery of post ACL-R subjects.

subjects’ activities during the rehabilitation period. Data logging from all sensors (motion and EMG) was performed by transmitting the sensor data through two base-stations connected to the laptop. Once the data from different sensors were transmitted, the processing was done by using system software developed in MATLAB 7.0. III. METHODOLOGY The hardware/software codesign for monitoring the recovery of the ACL-R subjects is shown in Fig. 2. In order to make a decision about the recovery state and performance of a subject, first the input parameters are collected through the sensors and a video camera for each activity during a rehabilitation monitoring session and the system outputs (recovery class and visual biofeedback) are generated using the processed data. The system software has a layered architecture where each layer performs one or more tasks and the results are transferred to the next layer

In order to design and validate the system, kinematics and EMG signals were recorded for the healthy and postoperated subjects at various walking speeds. Each subject was setup with four motion sensors attached to identified positions on lateral aspects of his/her thighs (at two thirds up the tensor fascia latae) and shanks (on halfway up the surface of the tibia) using flexible Velcro straps and adhesive tape to note the angular rate and accelerations of lower limb extremities (see Fig. 3) [28]. The surface EMG signals were recorded by placing foam snap electrodes on four knee extensor and flexor muscles, including the vastus medialis, vastus lateralis, semitendinosus and biceps femoris on both legs of the subjects as recommended by the physiotherapists and the consultant for physical strength and conditioning (see Fig. 3). The placements of sensors and electrodes were done based on standard guidelines and a calibration procedure was adapted in order to minimize the set up errors [29], [30]. The motion sensors and EMG wires were connected, respectively, to the command module and the bioradio module, which were worn by the subjects using a waist-belt. After the sensors were setup, the KinetiSense and biocapture software systems were started simultaneously to record the knee kinematics and neuromuscular signals. For each session, the required speed was setup on the treadmill by the subject and the data collection commands were initiated in both software systems when s/he started walking. The 3-D angular rates and linear

MALIK et al.: MULTISENSOR INTEGRATION-BASED COMPLEMENTARY TOOL FOR MONITORING RECOVERY PROGRESS

Fig. 5. Fig. 4. Identification of HS to synchronize the kinematics and EMG data. (a) Knee flexion/extension and shank angular rate. (b) EMG envelope for a muscle and anteroposterior acceleration.

accelerations were recorded using KinetiSense software, and the EMG data and 2-D acceleration were noted using the biocapture software. The data transfer between the command module/bioradio and the base station was performed using the 2.4GHz wireless link. Upon completion of each session, the data from the KinetiSense and biocapture software systems were exported to MATLAB for further processing. Moreover, video recordings were also made of all testing sessions in order to store the lower limb movements corresponding to the gait cycles. B. Processing for Motion and EMG Signals The knee joint orientation measurements were obtained from each motion sensor unit placed on the thigh and shank segments of both legs. The sensors were aligned to provide knee flexion/extension “θ” using the trapezoidal integration of angular rates “ω” about the z-axis (see Fig. 3) with a complementary filter and a compensation for the constant bias error [31], [32]. The raw EMG signals for selected lower extremity muscles were bandpass filtered (20–450 Hz) using a fourth order Butterworth filter to remove the noise/motion artifacts and generate the required feature set. C. Data Segmentation and Synchronization Data segmentation of kinematics and EMG signals was performed prior to feature extraction and then different features were computed for each segment (gait phase). For ambulatory activities, each gait cycle was segmented by detecting the heel strike (HS). In motion sensors’ data, HS event was identified by using the sagittal angular velocity of the shank such that between two minima on either side of a peak in velocity curve, every second minimum indicates the HS event (see Fig. 4) [33]. In order to mark the gait cycle in EMG data, the HS event was identified by using anteroposterior acceleration from a 2-D accelerometer available in biocapture (see Fig. 4) [34]. In order to provide visualization of superimposed signals, both kinematics

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Knee flexion/extension during different phases of a gait cycle.

and EMG signals were synchronized and overlaid using the HS event. Once a gait cycle was detected and verified manually, the time-normalization of the data was performed in order to represent it between 0–100%. D. Integrated Feature Set Computation and Transformation Important kinematics and EMG features were extracted from subjects’ motion for collecting relevant data to classify recovery status and identify the intra and intersubject variability during the rehabilitation period. 1) Kinematics Features: Alterations have been reported in knee flexion/extension for ACL-R subjects even several months after surgery. These abnormalities occur during different phases of a gait cycle (see Fig. 5). Hence, different parameters from knee flexion/extension movements were extracted in order to generate the kinematics feature set. The kinematics feature set “K” for each ambulatory activity included nine parameters from knee movements (mean, maximum and minimum values for flexion/extension) for each phase of a gait cycle (averaged gait cycle per trial). Thus, a total of 21 features (three kinematics features × seven phases of a gait cycle) were computed from averaged gait cycle data for each trial of an ambulatory activity. For ith phase of a gait cycle (i = 1..7), a kinematics feature vector “VK ” can be represented as (1), where θim ax , θim in and θim ean represent the maximum, minimum and mean values of flexion/extension, respectively. VK = (θim ax , θim in , θim ean ).

(1)

2) EMG Features: In this study, a combination of time, frequency and time–frequency features have been used to generate a comprehensive feature set for the EMG data for selected muscles. Due to the nonstationary nature of the neuromuscular signals, the continuous wavelet transform (CWT) has been chosen to provide time–frequency features for the EMG data which have been proved effective in classification [35], [36]. The CWT of an EMG signal EMG(t) is defined in (2), where s represents the scale parameter, τ represents the translation diameter of time shifting and the basis function ψ ∗ is obtained by

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scaling the mother wavelet at time τ with scale s. 1 CWTEM G (s, τ ) = √ s

∞

EMG(t)ψ ∗

−∞



 t−τ dt. s

(2)

The MATLAB software was used to calculate the wavelet coefficients for the EMG signals by choosing Morlet mother wavelet (after testing different types of mother wavelets) with scales from 1–256 [36]. For each ambulatory activity, six wavelet coefficients at scales 8, 16, 32, 64, 128 and 256 were chosen from four identified muscles for calculating the four statistical features, namely, maximum and minimum of the coefficients values, mean frequency (MNF) and root mean square values (RMS) of coefficients, for each phase of a gait cycle. For frequency fi and power spectrum Pi , the MNF is calculated as shown in (3). n 

EMGM NF =

fi Pi

i=m n 

.

(3)

Pi

i=m

Thus, EMG feature set “E” consisted of feature vectors of a size of 672 features for each ambulatory activity trial for each subject (four EMG features × six coefficients × four muscles × seven phases of gait cycle). For ith phase of a gait cycle (i = 1..7), an EMG feature vector “VE ” can be represented as (4), where Cim ax , Cim in , CiM NF and CiRM S represent the maximum, minimum, mean frequency and RMS values of coefficient, respectively.   (4) VE = Cim ax , Cim in , CiM NF , CiRM S . A feature level integration was performed to generate a combined kinematics and EMG feature set “F.” The dataset for each ambulation activity consisted of “N” combined feature vectors (VK , VE ) where “N” depends on the total number of subjects included for an activity and the number of trials per activity (i.e., N = X subjects × Y trials per activity). A single gait cycle (averaged gait cycle for a trial) during each ambulatory activity was represented by a feature vector of length 693 (672 EMG features + 21 kinematics features). In order to reduce the length of these feature vectors for efficiently applying the intelligent pattern recognition technique, principal component analysis (PCA) has been used [37]. PCA reduces the dimension of dataset “F” by removing the redundancy in the data and replacing the group of variables with a new single variable while still not rejecting some of the features completely from the dataset. These new variables, called principal components (PCs), are the linear combination of kinematics and EMG features. PCA was carried out on the dataset for each ambulation activity and new transformed datasets “T” were generated using the reduced number of PCs for applying fuzzy intelligent techniques to determine the status of recuperation. Moreover, kinematics or EMG parameters with highest coefficient values for PCs were identified in order to note the features mainly contributing for classification of the recovery status of the ACL-R subjects.

Fig. 6. Structure of the adaptive neuro-fuzzy system for recovery classification.

E. Formation of Groups for ACL-R Subjects The recovery progress of two or more ACL-R subjects may differ due to physiological variations and the coopers/noncoopers phenomenon. In order to account for such patient-to-patient variations, the ACL-R subjects were divided into groups on the basis of their current recovery condition and similarities in the kinematics and EMG parameters extracted in this study. A semiautomated process, involving fuzzy clustering and physiotherapist’s evaluation, was adopted to assign the subjects to different groups. Two groups of the ACL-R subjects were formed based on the recorded data and evaluations performed by the physiotherapists. The first group (group A) consisted of twelve ACL-R subjects who were within five months of surgery and the second group (group B) consisted of eight ACL-R subjects who were from six to twelve months of surgery. Group C consisted of ten healthy subjects. F. Recovery Classification The recovery analysis of the subjects was performed using ANFIS (see Fig. 6). The ANFIS is a fuzzy Sugeno model that adapts the membership function parameters using a neural network and learns from the available dataset [38]. The variations in the kinematics signals and the nonstationary nature of the EMG data make the recovery assessment task challenging. The ANFIS is more useful for building models for such inputs. It can effectively identify stochastic changes in the bio-signals, and can also deal with impreciseness in the measurements and variations due to the subjects’ physiological conditions [39]. In our approach, an ANFIS was designed for each walking speed based on the transformed feature set consisting of the integrated parameters (PCs) from knee flexion/extension and EMG data for four muscles during different gait phases. The number of input nodes depends on the selection of number of PCs which are represented by the bell-shaped membership functions with grade between 0 and 1 [see (5)]. μPC i (x) =

1 1 + {((x − ci )/ai )2 }b i

(5)

where ai , bi and ci are the parameters of the membership function. The system adjusts the membership function (μ) parameters based on the given data, and the number of rules and the output of fuzzy rules are minimized by computing the firing

MALIK et al.: MULTISENSOR INTEGRATION-BASED COMPLEMENTARY TOOL FOR MONITORING RECOVERY PROGRESS

TABLE I PARTICIPANTS DETAILS (AGE, HEIGHT AND WEIGHT) Group

Age (mean ± std) years

Height (mean ± std) cm

Weight (mean ± std) kg

Healthy ACL-R

26.6 ± 4.05 28.0 ± 4.15

165.4 ± 3.10 166.0 ± 5.23

68.2 ± 11.90 65.2 ± 10.24

H. Visual Biofeedback Generation

strengths of the internal nodes. The overall output of an n-input system is given in (6) where Li is the previous layer’s output and wi is called the firing strengths of the rules (7).

y=

n 

n 

wi Li =

i=1

wi Li

i=1 n 

(6) wi

i=1

wi =

n

μij (xj ).

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(7)

j =1

The output of the system was the recovery class/status (A, B or C) for each walking speed. The subtractive-clustering method was used to partition the data due to large number of inputs, no requirement of setting the number of clusters in advance and noise robustness. It is a one-pass algorithm for estimating the number of clusters by finding high-density data point regions in feature space. The cluster center is the point with the highest number of neighbors. The learning parameters of the membership functions (premise parameters) and the output (consequent parameters) were tuned using a hybrid learning algorithm. This algorithm combines the least squares method and the gradient descent method, which make the convergence faster in the large search space. The forward pass (least square method) and backward passes (gradient descent method) were used to optimize the consequent and premise parameters, respectively. After determining the consequent parameters, the output of the ANFIS (recovery status) was calculated and the premise parameters were adjusted based on the output error using the back-propagation algorithm [38].

G. EMG Envelope Generation and Superimposing Signals The raw EMG data for different muscles with zero mean were full-wave rectified and low-pass filtered to generate linear envelopes. The linear envelopes provide useful information that can be used to assess the strength/activation of different muscles for inter and intrasubject comparison. Before comparing the EMG amplitudes, the data were normalized for each subject using the mean value of the signal for each stride for individual muscles and the results were represented as the percentages of the mean [40]. In order to superimpose the kinematics and EMG signals for the purpose of visual representation, the data were resampled to match their sampling rates.

EMG biofeedback has been found very useful in different medical diagnostic applications and for improving patients’ recovery [16], [41], [42]. As part of this research, a visual biofeedback system was designed for monitoring the ambulation of ACL-R subjects based on the processed motion and EMG data, and the videos recorded during different experiments. The biofeedback system not only provides a visualization of the variations and activation patterns of kinematics and EMG signals but it also presents the superimposition of both signals. Thus, physiatrists and physiotherapists can confirm or disconfirm the contraction of a subject’s various muscles with respect to his/her knee angle movements. While a subject walks, the estimated knee orientation in sagittal plane and EMG envelopes from different lower limb muscles are superimposed so as to observe the changes in both type of signals simultaneously. This overlaying of the data allowed monitoring of the knee flexion/extension as well as variations in muscle strength, activation timings and durations for different phases of each gait cycle, for both ACL-R and healthy legs. This process avoids the need to separately monitor each of these signals and generates combined signals while the subject is performing a rehabilitation/sports activity. The system software generates a range of useful subject-specific outputs, e.g., average knee flexion/extension during multiple gait cycles for the ACL-R leg, comparison of the ACL-R and ACL intact (ACL-I) legs, comparison of the strength variations for different flexors/extensors within the ACL-R leg and of these with the ACL-I leg muscles, superimposition of the knee flexion/extension and individual muscle plots, etc. It also presents a comparison of each individual with subjects from the other groups to assess intersubject parameter variations during the post ACL reconstruction recovery regimen. The visual analysis was further enhanced by recording and displaying a video of each subject’s affected lower limb during ambulation. Video gait analysis is commonly used in clinical settings to determine functional impairment and to evaluate the rehabilitation treatment. The recorded video of the movements of the lower limbs can also be shown (at slow speed with different options if required, e.g., frame by frame, forward/backward) together with the corresponding knee angle and EMG patterns for each gait cycle, to physiotherapists/physiatrists so that they can observe any alterations in the knee dynamics of ACL-R subjects. Hence, corrective measures and muscle conditioning can be applied by the physiotherapists/physiatrists in order to improve the recovery status of the knee and monitoring of objective rehabilitation progress can be used as a motivation tool for ACL-R subjects. This visual biofeedback can be used as an adjunct to the existing ACL recovery monitoring mechanisms. IV. EXPERIMENTAL SETUP A. Subjects Ten healthy control subjects (six males, four females) and 20 unilateral ACL-R (15 males and five females) subjects were recruited from the Performance Optimization Center in the Ministry of Defense and the Sports Medicine and Research Center in

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the Ministry of Culture, Youth and Sports, Brunei Darussalam. The vital statistics of the participants are shown in Table I. All subjects read and signed an informed consent form and all procedures were carried out according to the ethics guidelines approved by Universtit Brunei Darussalam’s Graduate Research Office and Ethics Committee. B. Experimental Procedure All of the subjects walked on a motorized treadmill at three speeds from 4 to 6 km/h. During each session, every subject performed two trials for each walking activity. The number of trials was chosen so as to generate ample training and testing data for the ANFIS classifiers and also to account for the intrasubject variability in walking patterns. The data were collected for each speed for a duration of 30–35 s and ten gait cycles per trial were marked (discarding the data from the first and last few gait cycles) for further processing. The kinematics and EMG signals were recorded simultaneously during all experiments. While data were collected from both lower limbs for ambulation activities for providing visual biofeedback, but kinematics and neuromuscular features were generated only for one leg (operated leg for the ACL-R subjects and randomly selected leg for the healthy subjects). Thus, a dataset of knee dynamics and neuromuscular signals for each walking speed was prepared for each subject.

Fig. 7. Membership functions for three inputs of the ANFIS model (a) before and (b) after training for walking at a speed of 5 km/h.

V. RESULTS A. Recovery Status Classification The goal of the recovery status classification process was to train and test the ANFIS based models for classifying the input patterns of subjects during various ambulation activities to one of three groups (group A—ACL-R subjects whose recovery conditions were within five months of surgery, group B—ACL-R subjects whose recovery conditions were from six to twelve months after surgery, and group C—healthy subjects). The ANFIS classifiers were designed using MATLAB 7.0 for three different walking speeds of the subjects, based on the transformed feature sets. In order to optimize the classifiers’ performance and reduce the computation time for training, each classifier was designed with a reduced pattern set of selected PCs as an input vector and the relevant group (group A, group B and group C) as the target output. The datasets were randomly divided into training data (75%) and testing data (25%) for the purpose of training and verifying the accuracy of the classification for each ANFIS network. Each ANFIS was trained for 100 epochs with an initial value of 0.01 for step size. The training and testing were repeated ten times for cross validation. Fig. 7 shows the initial (before training) and final (after training) membership functions of three inputs (PCs) for walking at 5-km/h speed. The effect of number of PCs (included in training/testing pattern sets) on the classification performance of the ANFIS models for three ambulation activities is shown in Fig. 8. The highest classification accuracies (see Fig. 9) were found using four, three and three PCs for 4, 5 and 6 km/h walking speeds, respectively. The number

Fig. 8. Overall classification accuracy with respect to number of PCs included for training/testing of the ANFIS models for ambulation activities.

Fig. 9. Maximum overall classification accuracy of the ANFIS for ambulation activities.

of rules generated by these ANFIS models were eight, three and four for 4, 5 and 6 km/h walking speeds, respectively. The EMG/kinematics features with the highest coefficient values in these PCs are presented in Table II. The performance of each ANFIS was evaluated by computing a confusion matrix, and the specificity, sensitivity and F-measure for each class for all walking speeds were determined. The performance evaluations for all the systems are shown in Table III.

MALIK et al.: MULTISENSOR INTEGRATION-BASED COMPLEMENTARY TOOL FOR MONITORING RECOVERY PROGRESS

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TABLE II EMG/KINEMATICS FEATURES WITH HIGHEST COEFFICIENT VALUES FOR THE SELECTED PCS FOR EACH AMBULATION ACTIVITY Activity Walking at 4 km/h

Walking at 5 km/h

Walking at 6 km/h

PC

Gait Phase

Scale

Feature

1 2 3 4 1 2 3 1 2 3

1 1 4 6 2 3 3 5 4 6

64 32 256 128 16 16 32 16 32 64

MNF of BF MNF of VM Min. CWT Coeff. of VM MNF of BF Min. CWT Coeff. of VM Min. CWT Coeff. of ST Min. CWT Coeff. of ST Min. CWT Coeff. of BF Min. CWT Coeff. of ST Min. CWT Coeff. of ST

Fig. 11. Knee flexion/extension variation in the subjects at 4 km/h—Mean angle values of a healthy and ACL-R subjects.

TABLE III ANFIS PERFORMANCE EVALUATION: MEAN SENSITIVITY, SPECIFICITY AND F-MEASURE FOR DIFFERENT ACTIVITIES FOR ALL GROUPS Activity

Walking at 4 km/h

Walking at 5 km/h

Walking at 6 km/h

Class

Specificity (%)

Sensitivity (%)

F- Measure (%)

Group A Group B Group C Group A Group B Group C Group A Group B Group C

97.75 99.63 100.00 98.30 100.00 99.68 99.90 99.87 100.00

99.76 98.56 99.98 99.83 99.43 100.00 99.92 99.95 100.00

98.56 99.15 99.95 98.86 98.20 99.84 99.85 99.90 100.00

Fig. 10. Knee flexion/extension variation in the subjects at 4 km/h—Mean angle values in the ACL-R versus ACL-I leg for an ACL-R subject two months after surgery and for a healthy subject.

B. Visual Biofeedback The biofeedback system provides visual monitoring of individual and superimposed signals (kinematics and EMG) to physiotherapists, physiatrists and clinicians, as well as to the subjects. This visual biofeedback has been found effective in improving the knee extension and muscle strength for ACL-R subjects. The visual representation of the different signals and their impact on ACL recovery are described below. 1) Variations in Kinematics Parameters: Intra and intersubject variations in knee kinematics during walking activity can be visually represented using the developed system. The variations in knee flexion/extension of the ACL-R and ACL-I legs can be compared at different walking speeds for each subject. Fig. 10

Fig. 12. Knee flexion/extension variations in the ACL-R leg of a subject two months after surgery at 6 km/h.

Fig. 13. Knee flexion/extension variations in the ACL-R leg of a subject ∼12 months after surgery at 6 km/h speed.

plots the average knee flexion/extension during 20 gait cycles of an ACL-R subject two months after surgery. During the stance phase (single limb support from 20%–50% of a gait cycle), the ACL-R limb exhibits restricted extension. ACL injury also affects the contralateral limb also as shown in Fig. 10, where the knee flexion/extension of the ACL-I limb of the same subject deviates from normal flexion/extension during the stance phase. These deviations were more visible at higher walking speeds. Intersubject comparisons can also be performed to study the differences in kinematics among subjects at the various stages of recovery (see Fig. 11). There are noticeable differences in knee extension among subjects at different recovery stages during the stance phase and at peak knee flexion. Stride-to-stride variations, and the mean and standard deviation for flexion/extension during a trial are also shown for each subject (see Figs. 12 and 13).

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Fig. 15. Superimposition of the knee flexion/extension and vastus lateralis EMG envelope for a healthy subject while walking at a speed of 4 km/h.

Fig. 14. Comparison of muscle characteristics for the vastus lateralis and vastus medialis in the ACL-R leg of a subject one year after surgery at a speed of 4 km/h. (a) Stride to stride variations for multiple gait cycles. (b) % of mean EMG and standard deviation for multiple gait cycles.

Higher standard deviation values were generally found for subjects in group A (see Fig. 12) than for group B (see Fig. 13). 2) Variations in EMG Parameters and Superimposition of Signals: Comparison of the different characteristics of the muscles (e.g., activation timing, duration, and strength) within the subject’s legs (whether same or different legs) at various walking speeds can be represented visually using the designed system. Before comparing the intra and intersubject variations in the EMG data, the signals were mean normalized for all muscles. As an example, Fig. 14 depicts the comparison of the vastus lateralis and vastus medialis muscles for a subject one year after ACL reconstruction. Similarities in the activation timings of both muscles were observed, but the muscle strengths varied which suggests that the vastus medialis is still weak in the ACL-R leg even one year after surgery. For healthy subjects, by contrast, these variations were generally found to be much smaller than for the ACL-R subjects in group A and group B. The poor muscle strengths may also result in alterations in the knee kinematics so superimposition of the knee kinematics and EMG envelopes of different muscles can also be used to examine the coordination and correlation between these two signals. Superimposition of the knee flexion/extension and vastus lateralis EMG envelope for a healthy subject while walking at a speed of 4 km/h is shown in Fig. 15. The vastus lateralis and vastus medialis help in extending the knee joint, and by overlaying their EMG and knee flexion/extension, the contributions of these muscles to the gait patterns can be determined. In a normal healthy leg (see Fig. 15), during most of the gait cycles the knee extends properly because the vastus lateralis is at full strength.

Fig. 16. Activation timings and strengths of the vastus lateralis of the ACL-I leg versus knee flexion/extension for a subject two months after surgery.

Fig. 17. Activation timings and strengths of the vastus lateralis of the ACL-R leg versus knee flexion/extension for a subject two months after surgery.

However, during the first gait cycle (between 17.5–18 s) the reduced strength of the vastus lateralis causes restricted extension in the knee (the curve is a bit straighter after the peak in knee flexion marked by the first circle). The knee extension due to normal muscle contraction is marked by dotted circles during the next two gait cycles where the knee extension (small valley) can be seen after the knee flexion (small peak). The activation timings and strengths of the vastus lateralis in ACL-I/ACL-R legs superimposed on the corresponding knee flexion/extension of a subject approximately two months after surgery are shown in Figs. 16 and 17, respectively. These figures demonstrate an observable difference in the knee extension angle during the early stages of the subject’s gait cycle two months after surgery. The strength of the vastus lateralis muscles varies between the healthy and operated legs in this subject, causing restricted extension during all gait cycles. Similar overlays can

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Fig. 18. Superimposition of the knee flexion/extension and vastus lateralis EMG envelope for a recovered subject while walking at a speed of 4 km/h with a selection of frames from the video recording of lower limb movements during each gait phase.

be plotted for the hamstrings too. Thus, comparison of the kinematics and EMG signals from different muscles can be used, in conjunction with the statistical parameters, as a tool to identify normal/abnormal walking patterns and the contribution of these muscles in generating those patterns. In addition, the superimposed knee flexion/extension and EMG signals can be examined together with the video recorded of the movements of the lower limbs in the ACL-R subjects in order to further identify any abnormal movements during the walking activities. For each gait phase, the frames were extracted and shown to the physiotherapists/physiatrists so that they could observe the corresponding knee angle and muscle activation/strength patterns (see Fig. 18). C. Impact of Biofeedback Based on the visual biofeedback and the analysis of recovery stage, the exact muscles and gait phases for the ACL-R subjects were identified by the physiotherapist and the head of physical strength and conditioning, and focused training and exercises were instituted to restore the normal knee kinematics and required muscle strength for these subjects. The effectiveness of the biofeedback was evaluated by randomly assigning the ACL-R subjects recruited in group A to one of two groups: either with BF (n = 6) or without BF (n = 6). Similarly, the subjects in group B were also randomly allocated to one of two groups: with BF (n = 4) or without BF (n = 4). The data for these subjects were collected and monitored during the first session. In addition to following the same rehabilitation protocol as the other ACL-R subjects, visual representation of the

data for the subjects in the BF group were used to identify the muscle movements and knee dynamics and take appropriate measures. Different parameters (the average knee extension angle during the terminal stance, the peak knee flexion and the normalized peak values for the vastus lateralis, vastus medialis and hamstring muscle strengths) were noted during ambulation at different speeds for all the ACL-R subjects after a period of around six weeks, to evaluate the effects of the biofeedback. An independent sample student’s t-test, with p < 0.05 considered as the significance threshold, was used to test the differences between the groups. Significant differences (p < 0.05) were observed in the knee extension during the terminal stance and the peak knee flexion for the subjects in group A who were treated using BF as a complementary tool, in comparison with the subjected treated without BF. Moreover, significant differences (p < 0.05) were also noted in the normalized peak values for the vastus lateralis and vastus medialis muscles. However, no significant differences were noted between the two groups (with BF and without BF) in the normalized peak values for the hamstring muscle strengths. Significant differences (p < 0.05) were observed only for the average knee extension angles during the terminal stance and the normalized peak values of the strength of the vastus medialis in subjects in group B who were treated with BF compared to those treated without BF. VI. DISCUSSION The use of wireless body-mounted sensors and the integration of knee kinematics and neuromuscular signals have been found helpful in assessing the recovery progress of ACL-R subjects.

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The monitoring of bio-signals and their superimposition allow to estimate the coordination of knee kinematics and muscle movements during ambulation. The effect of each muscle on the variability of gait patterns can be evaluated for individual subjects using the proposed system. Moreover, this study suggests that the subjects’ recovery stages can be accurately identified using features extracted from the kinematics and neuromuscular data recorded during walking activities. The restoration of normal gait patterns and postural control are important measures for evaluating the progress of post ACLR subjects during convalescence. Persisted alterations in gait parameters can result in cartilage degeneration and osteoarthritis. Based on the kinematics and neuromuscular data collected during the walking activities and their feature level integration using PCA, the application of ANFIS model has been found quite effective in analyzing and classifying the recovery stage. Due to fuzzy system, the ANFIS can account for the variability in the kinematics and EMG data. The ANFIS performance analysis shows that the system performs well when classifying higher walking speeds as at these speeds the knee and muscle movements are more clearly identifiable. This suggests that the monitoring of other dynamic activities such as running and oneleg jumping can be useful in differentiating between healthy and ACL-R limbs using ANFIS. The application of PCA has been proved as a means to reduce the large feature set to a small number of PCs and thus optimizing the classification performance of ANFIS models. PCA also provided a hint about the main contributing features for classifying the recovery stage of the ACL-R subjects (see Table II). Based on the available subjects’ data, mostly features from EMG data were found with high coefficient values suggesting that the EMG signals from quadriceps and hamstring muscles can be considered for analyzing the recovery performance of ACL-R subjects. Only one feature with the highest coefficient value for each selected PC has been reported in Table II while a more detailed analysis can be performed by considering other features also. Biofeedback can help in identifying muscle and knee joint abnormalities during ambulation in each phase of a gait cycle for taking targeted and timely corrective measures. Variations in the knee angle between the ACL-R and ACL-I legs can be monitored and the impact of ACL injury on the contralateral leg can also be examined. The visual analyses facilitate the identification of intra and intersubject variations in the kinematics and neuromuscular signals during the different experiments. The superimposed/overlaid bio-signals can be used to identify the correlations between muscle recruitment and knee joint movements. These correlations can assist in detecting those muscles causing the changes in the knee flexion/extension and pinpointing the activation of different muscles during different ambulation activities. The activation timings and duration properties of the muscles, measured by the system while the healthy subjects were walking normally are consistent with previously reported research and standards [43]. While the classification performance of the ANFIS is excellent with limited number of athletes/subjects recruited through Sports Medicine and Research Center, and Performance Optimization Center of the Ministry of Defense in Brunei, it is

IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 20, NO. 5, OCTOBER 2015

essential to further test the system for clinical applications by including more subjects with additional parameters. The effects of gender, the types of protocol followed by subjects, the types of graft used and other physical parameters were not considered in the design of present study. ACL injury not only affects knee movement in sagittal plane but also alters the knee dynamics in other two planes (rotation and abduction/adduction) [4] so the inclusion of these kinematics movements in addition to the existing parameters could easily be incorporated into the study to enhance the system’s assessment of the progress of recovery. Successful validation of the developed system using athletes/soldiers, trainers and physiotherapists will allow further investigation in other directions. The system could be enhanced to provide online biofeedback to the clinicians and subjects after training is complete, by fully automating the synchronization of the motion and EMG signals and improving the features’ computation steps. VII. CONCLUSION AND FUTURE STUDY This paper describes a new method for monitoring ACL recovery based on the use of intelligent mechanism and the integration of knee kinematics and neuromuscular parameters. The proposed intelligent integrated system makes use of wearable sensors to record the knee movements and EMG data and then classifies the recovery status of the ACL-R subjects using ANFIS based on combined kinematics and EMG features. The results of the classification indicate that the system can successfully identify the recovery stage of the ACL-R subjects based on walking activity. Moreover, visual analysis of the signals can also help in identifying knee joint abnormalities and muscle strengths in each gait phase. The system can be used as a complementary tool in conjunction with existing rehabilitation monitoring mechanisms for ACL-R subjects. It enables clinicians, trainers and physiotherapists to objectively monitor the rehabilitation progress of subjects during different convalescence stages. In future, further clinical testing of the system will be done by including other activities (balance testing, running, one-leg jumping etc.) and additional EMG/kinematics features, as part of an ongoing project. Moreover, the effects of other muscles on recovery assessment and the design of the ANFIS will also be investigated. ACKNOWLEDGMENT The authors would like to thank M. Anderson who assisted in the proof-reading of the manuscript. REFERENCES [1] L. S. Lohmander, A. Ostenberg, M. Englund, and H. Roos, “High prevalence of knee osteoarthritis, pain, and functional limitations in female soccer players twelve years after anterior cruciate ligament injury,” Arthritis Rheumatism, vol. 50, pp. 3145–3152, 2004. [2] H. G. Potter, S. K. Jain, Y. Ma, B. R. Black, S. Fung, and S. Lyman, “Cartilage injury after acute, isolated anterior cruciate ligament tear: Immediate and longitudinal effect with clinical/MRI follow-up,” Amer. J. Sports Med., vol. 40, pp. 276–285, 2011. [3] R. Papannagari, T. J. Gill, L. E. Defrate, J. M. Moses, A. J. Petruska, and G. Li, “In vivo kinematics of the knee after anterior cruciate ligament reconstruction: A clinical and functional evaluation,” Amer. J. Sports Med., vol. 34, pp. 2006–12, 2006.

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