Monitoring Walker Assistive Devices - MDPI

sensors Article

Monitoring Walker Assistive Devices: A Novel Approach Based on Load Cells and Optical Distance Measurements † Vítor Viegas 1,2, *, J. M. Dias Pereira 1,2 , Octavian Postolache 1,3 and Pedro Silva Girão 1,4 1 2 3 4

* †

Instituto de Telecomunicações, 1049-001 Lisboa, Portugal; [email protected] (O.P.); [email protected] (P.S.G.) ESTSetúbal, Instituto Politécnico de Setúbal, 2910-761 Setúbal, Portugal; [email protected] ISCTE—Instituto Universitário de Lisboa, 1600-077 Lisboa, Portugal Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal Correspondence: [email protected]; Tel.: +351-265-790-000 This paper is an extended version of our conference paper: Dias Pereira J.M., Viegas V., Postolache O., Girão, P.S. Combining distance and force measurements to monitor the usage of walker assistive devices. 2017 IEEE International Instrumentation and Measurement Technology Conference, Torino, Italy, 22–25 May 2017.

Received: 14 December 2017; Accepted: 7 February 2018; Published: 10 February 2018

Abstract: This paper presents a measurement system intended to monitor the usage of walker assistive devices. The goal is to guide the user in the correct use of the device in order to prevent risky situations and maximize comfort. Two risk indicators are defined: one related to force unbalance and the other related to motor incoordination. Force unbalance is measured by load cells attached to the walker legs, while motor incoordination is estimated by synchronizing force measurements with distance data provided by an optical sensor. The measurement system is equipped with a Bluetooth link that enables local supervision on a computer or tablet. Calibration and experimental results are included in the paper. Keywords: force measurement; distance measurement; walker assistive device; gait analysis; usage monitoring

1. Introduction It is estimated that by the year 2025, in the United States and Canada, 25% of the population will be over 65 years old [1–3]. Moreover, it is expected that in the European Union the life expectance for the year 2060, for women and men, will be around 89 and 84.5 years, respectively [4]. Thus, topics related with the mobility of elderly people have an increased importance, particularly in which concerns the proper usage of mobility aiding devices [5,6]. The careful and conscientious use of these devices can avoid harmful injuries [7–9], namely the ones caused by elderly people falls. Besides the improvements related with elderly people, in terms of quality of life and extension of the time they can live autonomously at home, mobility adding devices are also very important for patients during rehabilitation processes. Several solutions were developed to address these problems [10–14] but many of them are too complicated, too expensive or too impractical for day-to-day applications. Many solutions make use of accelerometers and Inertial Motion Units (IMU) to extract kinematic parameters related with human gait [15–17]. However, these solutions require complex algorithms to improve measurement accuracy, in particular to mitigate the problems related with time integration of signals that contain persistent DC offsets (as it happens with low-cost accelerometers).

Sensors 2018, 18, 540; doi:10.3390/s18020540

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Solutions based on RAdio Detection and Ranging (RADAR) measurements are also referred to in the literature [18,19] and they are successful regarding the identification of gait patterns but not so efficient to obtain accurate measurements of walking distance. Usage of ultrasounds can also be a solution to obtain distance measurements [20]. However, ultrasound measurements are not directional and are affected by external factors such as environmental temperature and humidity. Several works [21,22] also refer the usage of image processing techniques to monitor gait. The main advantages of these methods are related with their non-invasiveness but there are strong limitations related with camera positioning and it is not possible, at all, to acquire data concerning force intensities. Concerning force measurements, used to evaluate walking balance conditions, the authors already reported a measurement system that uses Force Sensing Resistors (FSR) [23]. The results were acceptable but the main problems identified were related with lack of repeatability, poor robustness and sensors’ detrition. The positioning of the FSRs at the bottom of walker legs was critical to assure a uniform pressure distribution over the sensitive area, thus affecting the repeatability of the measurements over time. To overcome some of these limitations and to contribute to further advances in this research area, the authors propose a low-cost, easy-adaptable instrumented walker that detects risk situations motivated by unbalance and motor incoordination. This new proposal makes use of load cells to measure the force applied on the walker legs and Light Detection and Ranging (LIDAR) to measure distance. The acquired data is transferred through a Bluetooth link to a personal computer where it is processed and presented to a physiotherapist. A prototype based on a conventional walker with a four legs ground contact configuration was implemented and used for testing purposes. The measurement methods and technical solutions presented here can easily be applied to other mobility aiding devices, particularly walkers with different ground contact configurations such as wheeled walkers and rollators. The paper is organized as follows: Section 2 defines metrics to assess risk; Section 3 describes the implemented measurement system; Section 4 presents experimental results; and Section 5 draws conclusions. 2. Usage Metrics Two risk indicators, one related with force unbalance (I1 ) and the other related with motor incoordination (I2 ), are defined to monitor walker usage and detect potential dangerous situations. 2.1. Force Balance The first risk indicator (I1 ) has to do with the (un)balance of forces applied on the walker legs. Considerer the coordinate system illustrated in Figure 1 where the walker legs are numbered from 1 to 4 (as quadrants) and the y-axis points to the forward direction. According to this arrangement, the Centre of Forces (COF) is given by [24]: COFx =

W12 ( F1 − F2 ) + W43 ( F4 − F3 ) 2( F1 + F2 + F3 + F4 )

(1)

L( F1 − F4 ) + L( F2 − F3 ) 2( F1 + F2 + F3 + F4 )

(2)

COFy =

where Fk represents the magnitude of the force applied to each leg, W 12 and W 43 represent the distances between the front and rear legs respectively—which corresponds roughly to the mean walker width (W), L represents the distance between front and rear legs, which corresponds to the walker length.

𝛼=

𝐹1 + 𝐹2 + 𝐹3 + 𝐹4 𝐹𝑈

(4)

where the numerator represents the total force applied on the walker legs and FU represents the user weight. Alpha (α) is a weighting factor that takes values between 0 (when the walker is resting) and 2018, 540 3 of 16 1Sensors (when the18, walker is charged with all the weight of the user). Sensors 2018, 18, x FOR PEER REVIEW

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y P2 with α being given by:

𝐼1W (%) 12 = 100 ×

√(𝐶𝑂𝐹𝑥 )2 + (𝐶𝑂𝐹𝑦 )2 √(𝑊/2) P1 2 + (𝐿/2)2

×𝛼

𝑷𝟏 = (+ 𝑷𝟐 = (−

L 𝛼=

𝑾𝟏𝟐 𝟐 𝑾𝟏𝟐

𝑳

;+ ) 𝟐 𝑳

;+ )

𝟐 𝟐 𝐹1 + 𝐹2 + 𝐹3 + 𝐹4 𝑾𝟒𝟑 𝑳 ;− ) 𝐹𝑈 x 𝑷𝟑 = (− 𝟐 𝑾𝟒𝟑

(3)

(4)

𝟐 𝑳

where the numerator represents the total force applied on the 𝑷𝟒walker = (+ legs ;and − F)U represents the user 𝟐 weight. Alpha (α) is a weighting factor that takes values between 0 (when the𝟐walker is resting) and 3 4 user). 1 (when the walker isPcharged with all the weight ofPthe

W43

Figure 1. Coordinate system chosen for the measurement system. The black circles represent the y the Figure 1. Coordinate system chosen for measurement system. The black circles represent the walker legs (numbered as the quadrants in the Cartesian plane), Pk represents the application point of walker legs (numbered as the quadrants in the plane), Pk represents the application point W12 direction ofCartesian force F and the y axis indicates the movement. 𝑾𝟏𝟐 𝑳 P of forcekFk and the P y axis indicates the direction of movement. 2 1

𝑷𝟏 = (+

;+ )

𝟐 𝟐 𝑾𝟏𝟐 to𝑳the geometrical centre The unbalance indicator is defined as the deviation of the COF in relation 2.2. Motor Coordination 𝑷𝟐 = (− ;+ ) L 𝟐 𝟐 of the walker polygon: 𝑾 𝑳 walker movements 𝟒𝟑 The second risk indicator (I2) has to do q with the (in)coordination between
x 𝑷 = (− ; − ) 2 2 𝟑 COF + COF ( ) 𝟐 𝟐 x y and user gait. Considerer the state machine illustrated in Figure 2, first proposed by 𝑳 × α𝑾 (3) I1 (%) = 100 × q 𝑷𝟒 = (+ 𝟒𝟑 ; − )continuous arrows Winter D.A. [25,26], where the states represent(the W/2Gait )2 + Phases ( L/2 )2 (GP) 𝟐and the 𝟐

represent normal transitions between states. P with α being given by: 3

W43

α=

P4 F1 + F2 + F3 + F4 FU

(4)

Figure 1. Coordinate system chosen forforce the measurement system. The black represent the the user where the numerator represents the total applied on the walker legs andcircles FU represents walker legs (numbered as the quadrants in the Cartesian plane), Pk represents the application point weight. Alpha (α) is a weighting factor that takes values between 0 (when the walker is resting) and 1 of force Fk and the y axis indicates the direction of movement. (when the walker is charged with all the weight of the user).

2.2. 2.2. Motor Motor Coordination Coordination The second risk riskindicator indicator(I(I)2)has hastotododo with (in)coordination between walker movements The second with thethe (in)coordination between walker movements and 2 and user gait. Considerer the state machine illustrated in Figure 2, first proposed by user gait. Considerer the state machine illustrated in Figure 2, first proposed by Winter D.A. [25,26], Figure 2. Gait Phases (GP) during a walker step (the polygon delimits the support area). GP0: the Winterthe D.A. [25,26], where represent Phasesarrows (GP) and the continuous arrows where states represent thethe Gaitstates Phases (GP) andthe the Gait continuous represent normal transitions walkernormal is resting on the floor; GP1: thestates. walker is flying; GP2: the walker is on the floor waiting for the represent transitions between between states. injured foot to move forward; GP3: the injured foot is moving forward; GP4: waiting for the healthy foot to move forward; GP5: the healthy foot is moving forward.

The transitions are validated by sensor measurements as follows: 

T01 is enabled when F1 + F2 + F3 + F4 < WEIGHT_TH (threshold of minimum weight). This transition occurs when the total force measured by the load cells falls below the walker weight; in other words, when the walker is lifted in the air.

Figure 2. Gait Phases (GP) during a walker step (the polygon delimits the support area). GP0: the Figure 2. Gait Phases (GP) during a walker step (the polygon delimits the support area). GP0: the walker is resting on the floor; GP1: the walker is flying; GP2: the walker is on the floor waiting for the walker is resting on the floor; GP1: the walker is flying; GP2: the walker is on the floor waiting for the injured foot to move forward; GP3: the injured foot is moving forward; GP4: waiting for the healthy injured foot to move forward; GP3: the injured foot is moving forward; GP4: waiting for the healthy foot to move forward; GP5: the healthy foot is moving forward. foot to move forward; GP5: the healthy foot is moving forward.

The transitions are validated by sensor measurements as follows: 

T01 is enabled when F1 + F2 + F3 + F4 < WEIGHT_TH (threshold of minimum weight). This transition occurs when the total force measured by the load cells falls below the walker weight; in other words, when the walker is lifted in the air.

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The transitions are validated by sensor measurements as follows:

•

•

•

•

•

•

T01 is enabled when F1 + F2 + F3 + F4 < WEIGHT_TH (threshold of minimum weight). This transition occurs when the total force measured by the load cells falls below the walker weight; in other words, when the walker is lifted in the air. T12 is enabled if F1 + F2 + F3 + F4 > WEIGHT_TH AND d > TRAVEL_TH (threshold of minimum distance travelled forward). This transaction occurs when the walker touches the floor after traveling a distance greater than the minimum. T23 is enabled if COFx > RIGHT_TH or COFx < LEFT_TH depending on which foot is injured (left or right, respectively). This transaction occurs when the user steps forward with the injured foot and applies maximum force on the opposite side. The injured foot shall always be the first one to move. The type of disability must be defined in advance because it determines the threshold value. T34 is enabled if COFx < RIGHT_TH or COFx > LEFT_TH depending on which foot is injured (left or right, respectively). This transaction occurs when the user alleviates the force previously applied to the walker; in other words, when COFx returns to zero. T45 is enabled if COFx < LEFT_TH or COFx > RIGHT_TH depending on which foot is injured (left or right, respectively). This transaction occurs when the user steps forward with the healthy foot and applies maximum force on the opposite side. The healthy foot shall always be the last one to move. T50 is enabled if COFx > LEFT_TH or COFx < RIGHT_TH depending on which foot is injured (left or right, respectively). This transaction occurs when the user alleviates the force previously applied to the walker; in other words, when COFx returns to zero.

If the machine passes through all the states successfully then the step is marked as “good”; otherwise, the step is marked as “bad” and the counter B is incremented. The incoordination indicator is then computed as: B (5) I2 (%) = 100 × N where N is a moving window covering the last steps (defaults to 10). 3. Measurement System The measurement system includes four load cells to sense the force applied on the walker legs, a LIDAR to measure the travelled distance, a data acquisition board with Bluetooth link and software to process and present data. The apparatus was installed on a pick-up standard walker [27] with the following characteristics: width between front legs (W 12 ) equal to 52 cm, width between rear legs (W 43 ) equal to 53 cm, length between front and rear legs (L) equal to 45 cm, adjustable height between 78 cm and 90 cm in increments of 2.5 cm. 3.1. Force Sensors Force measurements were initially done by FSRs as described in [23]. Unfortunately, the installation of these sensors was very tricky making it very difficult to guarantee that each sensor had the same mechanical coupling to the process and, consequently, the same sensitivity. To overcome this problem, FSRs were replaced by load cells. Each load cell was attached to the extremity of a walker leg using a dedicated plastic adapter grown on a low-cost 3D printer (see Figure 3). The cell contains four strain gauges fixed to a small body of aluminium (55.3 × 12.7 × 12.7 mm) that supports 20 kgf. Other characteristics include: rated output = 1 ± 0.15 mV/V for nominal capacity (20 kgf), non-linearity = 0.05% FS (Full Scale), hysteresis = 0.05% FS, output impedance = 1000 Ω, overload = 150% nominal capacity. The use of bending beam load cells seems awkward when compared to inline/axial load cells but the truth is that they are much cheaper making them the best choice for low-cost systems.

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The load cells were characterized using the fastening scheme shown in Figure 3b. Three legs of the walker were laid on the floor while the fourth leg was laid on a precision scale, model Spider 1S–60S from Mettler Toledo, all properly levelled. Static loads were applied to the walker frame by putting lead weights on it. The weight measured by the scale is equal to the force applied on the walker leg provided the leg does not move. The load cell under test was supplied with 5 V its output voltage was read using a 6 12 digits digital multimeter, model 2701 from Keithley. Figure 4 presents the Sensors obtained 2018, 18, x FOR REVIEW 5 of 16 results for aPEER given load cell (similar results were obtained for the other cells). Force

walker leg (aluminum tube) screw nut adapter A

B

Load cell

ground

(a)

(b)

Figure3.3.Load Loadcell cellattached attachedtotothe thewalker walkerleg: leg:(a) (a)Photo; Photo;(b) (b)Side Sideview. view. Figure

The load cells were characterized using the fastening scheme shown in Figure 3b. Three legs of the walker were laid on the floor while the fourth leg was laid on a precision scale, model Spider 1S–60S from Mettler Toledo, all properly levelled. Static loads were applied to the walker frame by putting lead weights on it. The weight measured by the scale is equal to the force applied on the walker leg provided the leg does not move. The load cell under test was supplied with 5 V its output voltage was read using a 6½ digits digital multimeter, model 2701 from Keithley. Figure 4 presents the results obtained for a given load cell (similar results were obtained for the other cells). 5,0

y = 0,2726x - 0,1273 R² = 0,9985

4,5 4,0

Vout (mV)

3,5 3,0

2,5 2,0

Figure 4. Load cell characterization.

1,5 1,0

Using linear interpolation, validated by an R-squared value close to 1, the following relationship 0,5 was found between the output voltage in mV (Vout) and the input force in kgf (F): 0,0

0

2

4

6

8

10 F (kgf)

12

Vout = 0.2726 × F − 0.1273

14

16

18

20

(6)

The offset error is strongly attenuated by the subtractive nature of Equations (1) and (2). Figure 4. Load cell characterization. The remaining error is cancelled by running a self-calibration routine (see Section 3.5.1 for more details).

Using linear interpolation, validated by an R-squared value close to 1, the following relationship 3.2. LIDAR was found between the output voltage in mV (Vout) and the input force in kgf (F): A low-cost LIDAR device [28] was used to perform distance measurements. The LASER (6) 𝑉𝑜𝑢𝑡 = 0.2726 × 𝐹and − 0.1273 associated with this device makes use of pulsed light modulation techniques to improve the measurement accuracy. main characteristics thesubtractive LIDAR include: working wavelength of 905 The offset error isThe strongly attenuated byofthe nature of Equations (1) and (2).nm, The maximum train lengthby of running 256 pulses, pulse repetition rate(see of 20 kHz, 3.5.1 accuracy better than remainingpulse error is cancelled a self-calibration routine Section for more details). 3.2. LIDAR A low-cost LIDAR device [28] was used to perform distance measurements. The LASER associated with this device makes use of pulsed light and modulation techniques to improve the

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2.5 cm, measurement range up to 40 m (much higher than the required for our application), I2C communication interface, PWM output signal with 3 V of amplitude and 10 µs of resolution. The characterization of the LIDAR was performed pointing the LASER to a moving target and measuring the duty cycle (δ) of the PWM signal. The sensor was fixed at one end of a graduated ruler while the target could move freely anywhere from 10 to 90 cm. The distance was measured directly from the ruler, with a resolution of 0.5 mm and the pulse width was measured using a universal counter, model PM6669 from Philips (Amsterdam, Netherlands). Figure 5 presents the obtained results.

Figure 5. LIDAR characterization.

Using linear interpolation, validated by an R-squared value close to 1, the following relationship was found between the duty cycle in % (δ) and the distance in cm (d): δ = 0.2017 × d + 6.6574

(7)

3.3. Signal Conditioning Each load cell is supplied at 5 V and conditioned by an instrumentation amplifier with gain 100 (AD623, Analog Devices, Norwood, MA, USA), followed by a non-inverting amplifier with gain 8 (MCP6272, Microchip, Chandler, AZ, USA) and an RC low-pass filter with a cut-off frequency of 20 Hz. The result is a smooth output swing from 0 to 4 V for forces between 0 and 20 kgf. The PWM signal of the LIDAR passes through a similar filter (RC, low-pass, 20 Hz) and a non-inverting amplifier with gain 5, giving an output swing from 1 to 4 V for distances between 0 and 100 cm. 3.4. Data Acquisition Data is acquired by a Bluno Nano board [29] attached to the walker body as shown in Figure 6. The board provides all the resources of an Arduino Nano, plus a Bluetooth 4.0 wireless interface, all contained in a low-cost, small-footprint package. The load cells are mounted on the walker legs, one on each leg and the LIDAR is suspended on a selfie-stick pointing to the injured foot. The five sensors (four load cells and the LIDAR) are sampled at 50 S/s and the resulting data stream is transmitted over the air to a host (computer or tablet) where it is processed. The Bluetooth link emulates a serial port on both sides allowing transmission speeds up to 1 Mbps. It should be underlined the low cost of the proposed solution: the load cells (4 × 8 €), the LIDAR (150 €), the Bluno Nano board (40 €), the conditioning circuits (30 €) and some mechanical adapters (40 €) made a total of 292 €, a very reasonable price when compared to other proposals [30].

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Figure Figure 6. 6. Instrumented Instrumented walker. walker.

The load cells are mounted on the walker legs, one on each leg and the LIDAR is suspended on 3.5. Data Processing a selfie-stick pointing to the injured foot. The five sensors (four load cells and the LIDAR) are sampled samples arriving data to the host are handled by over a high-priority thread that bufferswhere them at 50 The S/s and the resulting stream is transmitted the air to a worker host (computer or tablet) into memory. Later, when on the main threadawakes up, every 125sides ms inallowing the present case, the samples it is processed. The Bluetooth link emulates serial port on both transmission speeds are processed in batch, one by one. The buffer contains a variable number of samples, from 6 to a few up to 1 Mbps. more,It depending on the host’s that Thesolution: state machine (responsible classifying should be underlined theload low at cost of moment. the proposed the load cells (4 × 8for €), the LIDAR steps) executed all samples is refreshed only(30 8 times second, which isadapters enough (150 €),isthe Bluno for Nano board (40but €),the thescreen conditioning circuits €) andper some mechanical to the application good and fluidity. (40give €) made a total of 292 €, aresponsiveness very reasonable price when compared to other proposals [30]. Data processing consists in the calculation of the risk indicators according to the following procedures: 3.5. Data Processing samples arriving theCOF hostyare by a high-priority worker thread that buffers them 1. The Computation of COFxtoand by handled solving Equations (1) and (2). into memory. Later, when on the main thread wakes up, every 125 ms in the 2. Computation of the first risk indicator (I1 ) by solving Equations (3) andpresent (4). case, the samples are processed by (B) one.byThe bufferthe contains a variable number ofin samples, from 6 to a few 3. Detectioninofbatch, “bad”one steps running state machine represented Figure 2. more, depending on the host’s load at that moment. The state machine (responsible for classifying 4. Computation of the second risk indicator (I2 ) by solving Equation (5). steps) is executed for all samples but the screen is refreshed only 8 times per second, which is enough Allthe these tasks aregood done by the application “Spy Walker” that runs on the host side and is to give application responsiveness and fluidity. managed the physiotherapist. application of was developed in Visual Studio 2012 using the C# Data by processing consists in The the calculation the risk indicators according to the following programming language. Its main window offers the following options (see Figure 7): procedures: 1. • 2. • 3. • 4.

Computation COFx and COF y by Equations and (2). Login: Ask forofinformation about thesolving user including its(1) weight (needed to compute Equation (4)). Computation of theafirst risk indicator (I1the ) bysmart solving Equations (3) and (4). Connect: Establish Bluetooth link with walker. Detection Sensors: of “bad” Determine steps (B) byexperimentally running the state represented in Figure 2. the walker Calibrate themachine offset error of the load cells and Computation of the second risk indicator (I2) by solving Equation (5). weight (also needed for Equation (4)).

All these tasks are done by the application “Spy Walker” that runs on the host side and is managed by the physiotherapist. The application was developed in Visual Studio 2012 using the C# programming language. Its main window offers the following options (see Figure 7):   

Login: Ask for information about the user including its weight (needed to compute Equation (4)). Connect: Establish a Bluetooth link with the smart walker. Calibrate Sensors: Determine experimentally the offset error of the load cells and the walker weight (also needed for Equation (4)).

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•   •  

Stream Data: Open a sub-window where the physiotherapist can monitor online the usage of Stream Data: Open a sub-window where the physiotherapist can monitor online the usage of the walker. Stream Data: Open a sub-window where the physiotherapist can monitor online the usage of the walker. Disconnect: the walker. Close the Bluetooth link with the smart walker. Disconnect: Close the Bluetooth link with the smart walker. Disconnect: Close the Bluetooth link with the smart walker.

Figure 7. Main window of the Spy Walker application. Figure 7. 7. Main Main window window of of the the Spy Spy Walker Walker application. Figure application.

3.5.1. Calibrate Sensors 3.5.1. Calibrate Sensors The “Calibrate Sensors” sub-window (see of the the The “Calibrate Sensors” sub-window sub-window (see (see Figure Figure 8) 8) serves serves to to extract extract the the offset offset error error of measurement system. With the walker resting on the floor, with with no no external external forces forces applied, applied, the user measurement system. With the walker resting on the floor, the user registers the COF coordinates and the weight of the walker frame. These residues are offset errors registers the COF coordinates and the weight of the walker frame. These residues are offset errors that registers the COF coordinates and the weight of the walker frame. These residues are offset errors that will be cancelled later during normal operation. COF residues are subtracted from Equations (1) will cancelled later during normal operation. COF residues are subtracted from Equations (1) and (2), that be will be cancelled later during normal operation. COF residues are subtracted from Equations (1) and (2), while the weight is residue is subtracted from the numerator of Equation (4). while thewhile weight subtracted from thefrom numerator of Equation (4). and (2), theresidue weight residue is subtracted the numerator of Equation (4).

Figure 8. Calibrate Sensors sub-window. Figure 8. Calibrate Sensors sub-window. Figure 8. Calibrate Sensors sub-window.

3.5.2. Stream Data 3.5.2. Stream Data 3.5.2. Stream Data The “Stream Data” sub-window is where data processing takes place. The graphical interface is The “Stream Data” sub-window is where data processing takes place. The graphical interface is Thein “Stream Data” sub-window divided two main panels as shownisinwhere Figuredata 9: processing takes place. The graphical interface is divided in two main panels as shown in Figure 9: divided in two main panels as shown in Figure 9:  Balance (panel A): The COF is computed and the result is presented as a cross moving over a XY  Balance (panel A): The COF is computed and the result is presented as a cross moving over a XY graph. When the user loads his left side the cross moves toward negative values of X; when he • Balance (panel A): Theloads COFhis is left computed themoves resulttoward is presented as values a crossof moving over graph. When the user side theand cross negative X; when he loads his right side the cross moves toward positive values of X. The same applies for the aloads XY graph. When histoward left sidepositive the cross moves values X; his right side the the user crossloads moves values of toward X. The negative same applies forofthe front/back direction over thetheYcross axis,moves much toward like a positive joystick. values A vertical slidersame shows the when he loads his right side of X. The applies front/back direction over the Y axis, much like a joystick. A vertical slider shows the instantaneous value of the I1 indicator. for the front/back over the Y axis, much like a joystick. A vertical slider shows the instantaneous valuedirection of the I1 indicator.  Coordination (panel B):the TheI1state machine is executed and the current gait phase is identified. A instantaneous value of indicator.  Coordination (panel B): The state machine is executed and the current gait phase is identified. A set of six LEDs are turned on sequentially as the state machine moves forward the next phase. If set of six LEDs are turned on sequentially as the state machine moves forward the next phase. If the user passes through all the phases successfully, all the LEDs end up lighted and the step is the user passes through all the phases successfully, all the LEDs end up lighted and the step is marked as “good”. If the user violates any phase, the machine is reset to the first stage (GP0) and marked as “good”. If the user violates any phase, the machine is reset to the first stage (GP0) and

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•

Coordination (panel B): The state machine is executed and the current gait phase is identified. A set of six LEDs are turned on sequentially as the state machine moves forward the next phase. If the user passes through all the phases successfully, all the LEDs end up lighted and the step Sensors 18, x FOR PEER REVIEW 9 of 16 is2018, marked as “good”. If the user violates any phase, the machine is reset to the first stage (GP0) and the step is marked as “bad”. The number of “good” and “bad” steps is registered. A vertical the step is marked as “bad”. The number “good” and “bad” steps is registered. A vertical slider shows the instantaneous value of the Iof 2 indicator. slider shows the instantaneous value of the I2 indicator.

Figure 9. Stream Data sub-window. Panel A presents data related with force balance (the dark circles Figure 9. Stream Data sub-window. Panel A presents data related with force balance (the dark circles represent the walker legs) and panel B presents data related with motor coordination. represent the walker legs) and panel B presents data related with motor coordination.

4. Experimental Results 4. Experimental Results This section describes the preliminary tests executed to verify the effectiveness of the This section describes the preliminary tests executed to verify the effectiveness of the measurement measurement system and to get a first impression about the values of the risk indicators. A total of system and to get a first impression about the values of the risk indicators. A total of five tests were five tests were done involving signals from all sensors (load cells and LIDAR). The sensors were done involving signals from all sensors (load cells and LIDAR). The sensors were easily integrated in easily integrated in the commercial walker without need of any modification in its mechanical the commercial walker without need of any modification in its mechanical structure. The load cells structure. The load cells were installed in the terminal part of the walker legs and the LIDAR was were installed in the terminal part of the walker legs and the LIDAR was fixed in the frontal frame bar fixed in the frontal frame bar with the beam pointing to the user’s injured limb. with the beam pointing to the user’s injured limb. 4.1. COF COF Location Location 4.1. Twowell-known well-known lead lead weights weights were were applied applied in in precise precise locations locations of of the the walker walker frame frame to to compare compare Two the COF measurements against the expected values. Figure 10 shows the two arrangements made: on the COF measurements against the expected values. Figure 10 shows the two arrangements made: on the left, the 10 kgf weight was applied at the point (0, 200) mm and the 6 kgf weight was applied at the left, the 10 kgf weight was applied at the point (0, 200) mm and the 6 kgf weight was applied at the the point (−250, 0) mm; on the right, 10 kgf weight applied at point the point 0) mm point (−250, 0) mm; on the right, the the 10 kgf weight waswas applied at the (250,(250, 0) mm and and the 6the kgf6 kgf weight was applied at the point (0, 200) mm. For each arrangement, the measured value of COF weight was applied at the point (0, 200) mm. For each arrangement, the measured value of COF and and its distance the origin are presented, as the corresponding expected (theoretical) its distance to thetoorigin (r) are(r) presented, as wellas aswell the corresponding expected (theoretical) values. values. The relative error of r was chosen to assess the accuracy of the measurement system. The values obtained, below 5%, are acceptable taking into account that this is a lost-cost measurement system.

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Expected (theoretical) values: COF = (−93.8, 125.0) mm; r = 156.3 mm Measured values: COF = (−73.6, 132.6) mm; r = 151.7 mm Relative error of r: ηr = −2.9% (a)

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Expected (theoretical) values: COF = (156.3, 75.0) mm; r = 173.4 mm Measured values: COF = (147.1, 74.6) mm; r = 164.9 mm Relative error of r: ηr = −4.9% (b)

Figure Figure10. 10.COF COFmeasurements measurementsafter afterloading loadingthe thewalker walkerframe framewith withtwo twolead leadweights: weights:(a) (a)1010kgf kgfatat (0, 0)0) mm; (b)(b) 1010 kgf atat (250, 0)0) mm and 6 kgf atat (0,(0,200) (0,200) 200)mm mmand and66kgf kgfatat(−250, (−250, mm; kgf (250, mm and 6 kgf 200)mm. mm.

4.2. Computation of I1 (Assessment of Unbalance) The relative error of r was chosen to assess the accuracy of the measurement system. The values This test was5%, done with the collaboration a male user of weight and impaired gait obtained, below are acceptable taking intoofaccount that with this is75a kgf lost-cost measurement system. caused by an injury on the right lower limb. This user will be referred as “Wally” (code name) in the 4.2. Computation of I1 (Assessment of Unbalance) remaining of this paper. Wally was a “good” step user following recommendations of gait its This test wasinstructed done with to the make collaboration of a male with 75 the kgf of weight and impaired physiotherapist. Figure 11 presents the resulting plots of d, COF x and I1, which can be explained as caused by an injury on the right lower limb. This user will be referred as “Wally” (code name) in the follows: remaining of this paper. Wally was0instructed to make a “good” following  Gait phase was omitted because it is astep waiting state. the recommendations of its physiotherapist. Figure 11 presents the resulting plots of d, COF and I1 ,and which can be explained follows:  During gait phase 1 the walker is lifted inxthe air travels about 30 cm as (from d ≈ 30 cm to d ≈ 60 cm). • Gait phase 0 was omitted because it is a waiting state.  Gait phase 2 is a waiting state that ends when the user starts moving the injured foot (making • During gait phase 1 the walker is lifted in the air and travels about 30 cm (from d ≈ 30 cm to COFx cross LEFT_TH). d ≈ 60 cm).  During gait phase 3 the user transfers part of his weight to the left to compensate the lack of • Gait phase 2 is a waiting state that ends when the user starts moving the injured foot (making support on the injured foot [31]. While the injured foot moves forward, the distance returns to COFx cross LEFT_TH). the base value (around 30 cm) and COFx returns to the origin, thus making the machine enter in During • stage 4. gait phase 3 the user transfers part of his weight to the left to compensate the lack of support injured waiting foot [31].state While injured forward, distance  Gait phaseon4 the is another thatthe ends whenfoot themoves user moves the the healthy foot returns (makingto the base value (around 30 cm) and COF returns to the origin, thus making the machine enter in x COFx cross RIGHT_TH). stage 4.  During gait phase 5 the user transfers part of his weight to the right. The load applied during • phase Gait phase 4 is another that ends when the the healthy user moves healthy (making 5 is lower than thatwaiting appliedstate on phase 3 because foot the moves morefoot easily than COF cross RIGHT_TH). x the injured foot. The step ends when both feet stay side-by-side, making COFx return to the origin • and During gait the user transfers part of his weight to the right. The load applied during restart thephase state 5machine. phase 5 is lower than that applied on phase 3 because the healthy foot moves more easily than the injured foot. The step ends when both feet stay side-by-side, making COFx return to the origin and restart the state machine.

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Figure11. 11.Synchronized Synchronized plots d (top), x (middle) I1 (bottom) a “good” step. The Figure plots of dof (top), COFCOF and I1and (bottom) duringduring a “good” step. The square x (middle) square boxesgait indicate from 1 to 5.values The are threshold values= are WEIGHT_TH = 0.5 × boxes indicate phases gait from phases 1 to 5. The threshold WEIGHT_TH 0.5 × WALKER_WEIGHT, WALKER_WEIGHT, TRAVEL_TH 6 cm, LEFT_TH = −78 and RIGHT_TH = +78 mm. TRAVEL_TH = 6 cm, LEFT_TH = −78=mm and RIGHT_TH = mm +78 mm.

Therisk riskindicator indicatorI I1has hastwo twomaximums maximumsthat thatmatch matchthe theextreme extremevalues valuesofofCOF COF,x,when whenthe theuser user The x 1 moreunbalanced unbalancedand andrelies reliesmore moreon onthe thewalker. walker.The Thefirst firstmaximum maximumisisabove above30% 30%and andthe thesecond second isismore maximum is above 20%, meaning that the unbalance is greater when the user moves the injured foot. maximum is above 20%, meaning that the unbalance is greater when the user moves the injured foot. These results suggest that I 1 is strongly related with the posture of the user inside the walker frame These results suggest that I1 is strongly related with the posture of the user inside the walker frame but but more experiments, involving more impaired users, will be to needed topatterns identifyand patterns and more experiments, involving more impaired users, will be needed identify establish establish threshold values for I 1. threshold values for I1 . 4.3.Computation ComputationofofI2I2(Assessment (AssessmentofofMotor MotorIncoordination) Incoordination) 4.3. Thistest testwas wasdone donewith withthe thecollaboration collaborationofoften tennon-disabled, non-disabled,inexperienced inexperiencedusers userswith withages ages This between42 42and and70 70years yearsold. old.Since Sincenone noneofofthem themhave haveever everused usedaawalker walkerbefore, before,they theyall allreceived receivedaa between preliminary briefing, given by a physiotherapist, about how to use the device correctly (i.e., theyall all preliminary briefing, given by a physiotherapist, about how to use the device correctly (i.e., they became aware of the gait phases depicted in Figure 2). became aware of the gait phases depicted in Figure 2). Eachuser userwas wasinstructed instructedtotodo dotwo twowalks, walks,ofoftwenty twentysteps stepseach, each,over overaastraight straightline linedrawn drawnon on Each the floor. During the first walk the user did not receive any feedback at all; during the second walk the floor. During the first walk the user did not receive any feedback at all; during the second walk theuser userreceived receivedlive livefeedback feedback(audio) (audio)from fromthe theSpy SpyWalker Walkerapplication. application.The Thefeedback feedbackincluded includedthe the the classification of the step (“good” or “bad”) and the cause of the failure (“step aborted,” “injured foot classification of the step (“good” or “bad”) and the cause of the failure (“step aborted,” “injured foot failedtotomove moveforward” forward”oror“healthy “healthyfoot footfailed failedtotomove moveforward”). forward”).Figure Figure12a 12aplots plotsthe therisk riskindicator indicator failed foruser user11during duringthe thetwo twowalks. walks.Similar Similarresults resultswere werefound foundfor forthe theother otherusers. users.Figure Figure12b 12bpresents presents I2I2for the average value of I 2 , per walk, with and without feedback, for the ten users. In all cases, the motor the average value of I2 , per walk, with and without feedback, for the ten users. In all cases, the motor coordination became better after the feedback given by the Spy Walker application. coordination became better after the feedback given by the Spy Walker application. The state machine worked well with no false “goods” and a few false “bads”. This can be justified by the fact that the validation of a “good” step is very demanding. Failures such as aborting a step by lifting the walker in the air or starting a step with the wrong foot were successfully detected.

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(b) Figure 12.Risk Riskindicator indicator I2 with and without feedback the Spy Walker (a) application: (a) Figure 12. I2 with and without feedback from the from Spy Walker application: Instantaneous Instantaneous values for Mean the user 1; (b)for Mean values for all users. values for the user 1; (b) values all users.

The state machine worked well with no false “goods” and a few false “bads”. This can be 4.4. COF Trajectories justified by the fact that the validation of a “good” step is very demanding. Failures such as aborting was instructed a sequence ofathree During that action, the coordinates a stepWally by lifting the walkertoinmake the air or starting step “good” with thesteps. wrong foot were successfully detected. of the COF were recorded and plotted as shown in Figure 13. The plot on the top (a) represents the 4.4. COF Trajectories waveforms of COFx and COFy over time; the plot in the middle (b) represents the trajectory of COF on the plane; and the plot at the bottom (c) represents the histogram of COFx . When the user is not Wally was instructed to make a sequence of three “good” steps. During that action, the walking, which is most of the time, the COF is around the origin. When the user makes a step, the COF coordinates of the COF were recorded and plotted as shown in Figure 13. The plot on the top (a) moves first to the 2nd quadrant, then to the 1st quadrant and finally returns to the origin. represents the waveforms of COFx and COFy over time; the plot in the middle (b) represents the The centre of the rectangle depicted in Figure 13b is located in (−37, 62) mm because the area trajectory of COF on the plane; and the plot at the bottom (c) represents the histogram of COFx. When covered on the 2nd quadrant is wider than that covered on the 1st quadrant. The skewness of the the user is not walking, which is most of the time, the COF is around the origin. When the user makes statistical distribution shown in Figure 13c is negative, equal to −0.93, because the tail on the left side a step, the COF moves first to the 2nd quadrant, then to the 1st quadrant and finally returns to the is longer than that on the right side. These are mathematical parameters that suggest (correctly) that origin. the user has an injury on the right lower limb and, for that reason, needs more support on his left side. The centre of the rectangle depicted in Figure 13b is located in (−37, 62) mm because the area covered on the 2nd quadrant is wider than that covered on the 1st quadrant. The skewness of the statistical distribution shown in Figure 13c is negative, equal to −0.93, because the tail on the left side is longer than that on the right side. These are mathematical parameters that suggest (correctly) that the user has an injury on the right lower limb and, for that reason, needs more support on his left side.

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(c) Figure 13. Behaviour COF during during aa sequence sequence of of three three “good” “good” steps: steps: (a) (a) COF COFx and COFy over time; Figure 13. Behaviour of of COF x and COFy over time; (b) trajectory of COF over the plane; (c) histogram of COF x. (b) trajectory of COF over the plane; (c) histogram of COF . x

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4.5. LIDAR Measurements 4.5. LIDAR Measurements Wally was instructed to make a sequence of ten “good” steps. During that action, the distance Wallyby was to makewith a sequence of ten “good” During action, theplotted distance measured theinstructed LIDAR, together the corresponding stepsteps. lengths, werethat recorded and as measured by the LIDAR, together with the corresponding step lengths, were recorded and plotted as shown in Figure 14. The step length is calculated by subtracting the maximum distance value shown in Figure The calculated subtracting the maximum distance value (acquired (acquired at the14. end ofstep gaitlength phaseis1) from thebyminimum distance value (acquired at the end of at the end of gait phase 1) from the minimum distance value (acquired at the end of phase 5). phase 5).

Figure 10 steps. steps. Figure 14. 14. Distance Distance values values and and step step lengths lengths for for walker walker gait gait containing containing 10

The user walked a total distance of 3.33 m, in steps varying between 37.1 cm and 30.3 cm, with The user walked a total distance of 3.33 m, in steps varying between 37.1 cm and 30.3 cm, with mean value equal to 33.3 cm and standard deviation equal to 2.3 cm. The low value of standard mean value equal to 33.3 cm and standard deviation equal to 2.3 cm. The low value of standard deviation suggests a regular walking pattern. The baseline of the LIDAR signal indicates the average deviation suggests a regular walking pattern. The baseline of the LIDAR signal indicates the average distance between the user and the walker frame. distance between the user and the walker frame. 5. Conclusions 5. Conclusions The paper presents an upgraded solution that combines force and distance measurements to The paper presents an upgraded solution that combines force and distance measurements to monitor the usage of walker assistive devices. The sensed data is transmitted over a Bluetooth link monitor the usage of walker assistive devices. The sensed data is transmitted over a Bluetooth link and processed on the host side to extract two risk indicators: I1 representing the (un)balance of forces and processed on the host side to extract two risk indicators: I1 representing the (un)balance of forces applied on the walker legs and I2 representing the (in)coordination between walker movements and applied on the walker legs and I2 representing the (in)coordination between walker movements and user gait. Data processing and presentation is done by a Windows-based application managed by the user gait. Data processing and presentation is done by a Windows-based application managed by physiotherapist. the physiotherapist. The experimental results showed that the measurement system works as expected concerning The experimental results showed that the measurement system works as expected concerning the localization of the COF and the extraction of distance metrics. The risk indicators were computed the localization of the COF and the extraction of distance metrics. The risk indicators were computed successfully and the obtained values seem to be meaningful to assess the posture of the user inside successfully and the obtained values seem to be meaningful to assess the posture of the user inside the walker frame. The live feedback provided by the Spy Walker application can help inexperienced the walker frame. The live feedback provided by the Spy Walker application can help inexperienced users to manoeuvre correctly walker assistive devices. New mathematical indicators (geometrical users to manoeuvre correctly walker assistive devices. New mathematical indicators (geometrical and and statistical) may be extracted to characterize gait and infer about the physical condition of the statistical) may be extracted to characterize gait and infer about the physical condition of the user. user. It is important to refer that the proposed measurement system is a low-cost prototype that can It is important to refer that the proposed measurement system is a low-cost prototype that can easily be adapted to existing mobility aiding devices, including standard walkers, wheeled walkers easily be adapted to existing mobility aiding devices, including standard walkers, wheeled walkers and rollators. and rollators. Acknowledgments: The work was supported by Fundação para a Ciência e Tecnologia, project PTDC/DTPDES/6776/2014. Author Contributions: The idea of monitoring walker usage by applying force sensors on the walker legs is the result of previous work from all authors. J. Pereira proposed adding the LIDAR to correlate distance and force measurements. V. Viegas and J. Pereira built the measurement system and performed the experiments.

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Acknowledgments: The work was supported by Fundação para a Ciência e Tecnologia, project PTDC/DTP-DES/6776/2014. Author Contributions: The idea of monitoring walker usage by applying force sensors on the walker legs is the result of previous work from all authors. J. Pereira proposed adding the LIDAR to correlate distance and force measurements. V. Viegas and J. Pereira built the measurement system and performed the experiments. O. Postolache and P. Girão analyzed the data and suggested improvements. All authors contributed to writing of the article. Conflicts of Interest: The authors declare no conflict of interest.

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