Study and implementation of a wireless accelerometer network for gait ...

Study and implementation of a wireless accelerometer network for gait analysis J. Stamatakis, P. Gérard, P. Drochmans, T. Kezai, B. Caby, B. Macq and D. Flandre UCL, Electrical Engineering Department, Place du Levant 3, 1348 Louvain-la-Neuve, Belgium Abstract — The purpose of this work is to develop a new wireless system implementing a network of accelerometers, based on the IEEE communication Std. 802.15.4, for gait analysis. 3-axis accelerometers are indeed well adapted for characterizing gait features. The IEEE 802.15.4 standard enables 250 kbps data transfers with very low power consumption. Our network of 10 such RF nodes with 3-axis accelerometers can record accelerations up to ± 10 g and shocks up to 50 Hz for tests on walking and running subjects. Accelerometers have been calibrated according to battery voltage and temperature variations. The average node power consumption is 14 mA, which allows 55 hours of use with two AAA batteries. A base station, connected to a PC, controls the whole 10 nodes system and collects the recorded data. Validation tests have shown that the system handles possible transmission problems. The system is robust and allows real time visualization of accelerations. The system could be easily reconfigured to incorporate other sensors.

only in their beginnings. So, we have developed a wireless accelerometer network based on the IEEE 802.15.4 standard. II. IMPLEMENTATION A. Hardware We have developed a network of 10 wireless electronic modules to place on a subject body and one wireless base station connected to a computer. Every module is composed of a 3-axis accelerometer, an 802.15.4 radio module with a microcontroller and an antenna.

Keywords — 802.15.4, accelerometer, gait, sensor, wireless

I. INTRODUCTION Falls in the elderliness become a major concern in our societies because of their consequences. For example, the fear of falling can have psychosocial consequences, leading to social isolation [1]. Researches on gait and movement analyses are rapidly growing since a few decades with the appearance of new technologies. Fields of application range from falls to walking disorders related to obesity [2] or Alzheimer disease [3]. Gait disorder assessments are often made in hospital with tests like the Tinetti test [4] where an expert has to evaluate criteria to obtain a final score. This method leaves space to the subjectivity of the expert and leads to important inter-observation and inter-observer variations. Therefore it’s important to provide non-human objective observations to support the assessment. 3-axis accelerometers have many advantages for characterizing gait features, relative to other non-human systems. Indeed, they are small and lightweight so they can be positioned anywhere on subject body, allowing the monitoring of every movement even in special environments where systems with cameras are limited. Gait involves accelerations up to ± 10 g and shocks up to 60 Hz [5], which had to be taken into account during our development. So far, wired systems have demonstrated their abilities but wireless systems are

Fig. 1 Our battery powered electronic wireless module Every module is powered by two AAA batteries. The base station is connected to the computer through a RS-232 connection. The card dimensions without antenna and batteries are 3 cm by 6.3 cm. Fig. 1 presents one of our wireless electronic devices. B. The IEEE 802.15.4 Standard In 2003, the IEEE has issued a standard named 802.15.4 targeting simplicity, lightweight, and low cost with very low consumption for personal area networks (PAN) [6]. With such a standard, nodes powered with simple AA batteries could run for years at very low duty cycles. It enables 250

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kbps data transfers and defines the PHY and MAC layers. The other layers have to be defined by the user or by another protocol like Zigbee [7]. The PHY layer allows the use of the 2.45 GHz European ISM band. Two kinds of topologies are defined: star and peer-to-peer. C. Network and Application layers As the IEEE 802.15.4 defines only the two first layers, we have developed our own network and application layers. The base station is used as PAN coordinator in a star topology. The other modules are used as end-devices. To record a sample from each node at the same moment, they have to be synchronized. So, the sampling begins when the base station sends a broadcast. At this moment, every node starts to record acceleration data at the sampling frequency of 100 Hz and stores them in a buffer. This buffer contains first a sequence number and other information like the actual battery voltage. At every sampling moment, the node records the data from each of the 3 axes of the accelerometer which are coded on 10 bits, according to the effective accuracy of the 12 bits ADC. Given the maximum amount of data a frame can contain, the sampling frequency and the amount of data recorded every 10 ms, a buffer is full after 170 ms. The base station has then to collect the stored data from the different nodes, successively, from node 1 to node 10. Since all the buffers are full simultaneously, the nodes use two rotating buffers (one full and one in use) and the base station has to collect all the data from the full buffers before the end of the 170 ms period. The base station has thus 15 ms to collect data from one node, which leaves 20 ms at the end of the ring for clock skew inaccuracies. The division of the 170 ms ring in timeslots allows a node to be in the radio mode for only 15 ms, which reduces its power consumption. Within a timeslot the appropriate node sets its state to the radio receive mode and awaits a base station query. When received, the node sends the last full recorded buffer to the base station. There is no acknowledge sent back from the base station as retransmissions are not allowed, according to the timings. After the operation, the node leaves the radio mode and the base station waits till the end of the timeslot to query the next node. These different operations are shown in details in the section consumption. Since the communications occur in parallel with data recording, we have separated the two processes. The radio operations are handled by the microprocessor, the sampling operations by the ADC synchronized on a 10 ms timer and by the direct memory access (DMA) controller which has to transfer data from the ADC to the memory. This separation guarantees the 100 Hz sampling rate.

Finally, when the base station has collected a data buffer, it sends it to the computer where a Java application allows the visualization of the accelerations of every node. Thanks to the sequence numbers, the application knows when a buffer is lost and takes it into account to maintain synchronization between all the nodes. To end the sampling, the base station sends a termination broadcast. D. Calibration The accelerometers deliver, for each axis, an output voltage between 0 and 3 V as output. On the computer, voltages are transformed back in accelerations, using two parameters: the 0 g output voltage and the sensitivity. Those parameters vary from an accelerometer to another and thus need to be calibrated once before use. But those parameters also vary with temperature and battery voltage. Temperature variations can be neglected according to the calibration errors. Variations due to battery voltage can amount to 3.5 g for 0.8 V shift. The output offset voltage and sensitivity linear scaling with applied supply voltage is called ratiometricity. The battery voltage is recorded for every node and buffer in order to adapt the parameters and always have correct accelerations even if the level of the supply voltage is lower. E. Consumption In order to evaluate a node consumption and the battery lifetime, we have made consumption measurements. The different phases of a timeslot are shown in Fig. 2 and the legend is presented in Tab. 1.

Fig. 2 Different phases of a node timeslot – legend in Tab. 1

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Study and implementation of a wireless accelerometer network for gait analysis

The radio receive mode leads to a 31.3 mA consumption and the radio emission power can be chosen thanks to a programmable register. The higher the power, the higher the consumption and the communication range. Tab. 1 Consumption and duration of the different phases of a node timeslot – Fig. 2 legend Phase

Consumption

Duration

1

Radio receive mode

31.3 mA

2.44 ms

2

Switch receive-send

18.3 mA

160 μs

3

Radio send mode (ack)

21.4 mA

760 μs

4

Switch send- receive

18.3 mA

160 μs

5

Radio receive mode (end)

31.3 mA

480 μs

6

Switch receive-send

18.3 mA

160 μs

7

Radio send mode (data)

21.4 mA

4 ms

8

Idle mode (radio off)

13.4 mA

161.84 ms

N°

According to the different phases consumption and duration, the mean consumption is 14.18 mA. The use of alkaline batteries allows the linear discharge hypothesis which leads to 55 hours of use with a set of two AAA batteries. The range is about 15 meters but could be increased by an optimal radio design.

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The first analyzed characteristic is the number of steps necessary to walk 12 meters. Its relation with the Tinetti Score is shown in Fig. 3. The frequency of the steps is also considered as an indicator of gait disorder [8] independently of the age of the subject [9]. Fig. 4 represents the step frequency versus the Tinetti score, showing a growing tendency between the two variables. The only outlier point comes from the only young (22 year) man. Auvinet et al. [10] have found a significantly smaller frequency for male than for female subjects that can explain this distance with respect to the global tendency. The spectral entropy [11] (SpecEn) is given by

SpecEn =

− 1 50 Hz ¦ PSDn ( f ) log[PSDn ( f )] (1) log(M ) f =0

where PSDn is the normalization of the PSD (Power Spectral Density) such that

¦ PSD ( f ) = 1 n

(2)

and M is the number of frequency bins. Fig. 5 shows the relation between SpecEn and Tinetti score. One can see a growing tendency, which can be explained by the fact that high SpecEn values corresponds to a whiter noise (a flatter spectrum), i.e. more vitality.

III. GAIT ANALYSIS A. Material and method As discussed in the introduction, most gait analyses and risk of falling assessments are subjective and highly depend on the analyzer. Therefore we introduce a rigid protocol of well defined tests in combination with the accelerometer network to enhance the stability of the results for inter and intra observer assessments. Two tests have been executed by elderly patients: walking 12 meters at preferred speed and a version of the Tinetti Test. The walking test is done in a hospital hall with start and stop lines on the floor. The patient is then driven on a wheelchair for the Tinetti Test [4]. The data are collected during the 2 tests and are analyzed off line. B. Preliminary results In this section, preliminary characteristics of the accelerometer signals that may assess gait disorder or risk of falling are presented. Therefore, the characteristics are compared to the score obtained by the Tinetti Test. Two 3axes accelerometers centered on right and left tight give the signals for analyses.

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Fig. 3 Relation between the number of steps to walk 12 meters and the Tinetti score for old peoples

The Median frequency (MF) of the signal is also analyzed in other contexts like MEG analysis [11]. The MF is defined as the frequency under which the half of the PSD power is contained. In Fig. 6, a linear dependence between the MF and the Tinetti score is observed.

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power consumption is 14 mA, allowing 55 hours of use with two AAA batteries. Analyses based on the data collected on PC by a central base station show relations between the Tinetti score and signal characteristics. The advantage of this assessment method is to avoid human subjectivity. The critical step of the protocol is the placement of the accelerometer on the body parts that can vary between care-givers. Therefore we need to confirm these preliminary results on a large population of subjects.

ACKNOWLEDGMENT Fig. 4 Step Frequency versus Tinetti score.

Our works are funded by the Région Wallone through the NANOTIC, CAVIMA, CROTALE and ICMD projects. The medical study is made in the university hospital ERASME (Université Libre de Bruxelles) under supervising of T. Peppersack and C.Berlemont.

REFERENCES 1.

Fig. 5 SpecEn versus Tinetti score

Fig. 6 Linear dependence between MF (Hz) and the Tinetti score IV. CONCLUSIONS AND FUTURE WORK We have developed a network of RF nodes with calibrated 3-axis accelerometers which can record accelerations up to ± 10 g and shocks up to 50 Hz, for gait tests on walking and running subjects. The average node

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Stalenhoef P, Diederiks J, Knottnerus J, Kester A & Crebolder H (2002) A risk model for the prediction of recurrent falls in community-dwelling elderly: A prospective cohort study. Journal of Clinical Epidemiology 55:1088–1094 2. Lai P.P.K, Leung A.K.L, Li A.N.M & Zhang M (2008) Threedimensional gait analysis of obese adults. Clinical Biomechanics. DOI 10.1016/j.clinbiomech.2008.02.004 3. O’Keeffe S.T, Kazeem H, Philpott R.M, Playfer J.R, Gosney M & Lye M (1996) Gait disturbance in Alzheimer’s disease : a clinical study. Age and Ageing 25:313-316 4. Tinetti ME (1986) Performance-oriented assessment of mobility problems in elderly patients J Am Geriatr Soc 34:119-126 5. Kavanagh, J.J & Menz, H.B (2007) Accelerometry : A technique for quantifying movement patterns during walking. Gait and Posture. DOI 10.1016/j.gaitpost.2007.10.010 6. IEEE Std 802.15.4 (2006) Standard for part 15.4, Wireless medium access control (MAC) and physical layer (PHY) specifications for low rate wireless personal area networks (LR-WPANs), New York 7. Baronti P, Pillai P, Chook V.W.C, Chessa S, Gotta A & Fun Hu Y (2007) Wireless sensor networks : A survey on the state of the art and the 802.15.4 and ZigBee standards. Computer Communications 30:1655-1695 8. Auvinet B, Maugars Y, Chaleil D http://www.entretiens-ducarla.com/publication.php?pub=fibro4&pg=disorders#stu 9. Auvinet B, Berrut, G, Touzard C, Moutel L, Collet N, Chaleil D, Barrey E (2002) Falls in the elderly : the need for teamwork through a network, Revue médicale de l’assurance maladie 33 (3) 10. Auvinet B, Berrut, G, Touzard C, Moutel L, Collet N, Chaleil D, Barrey E (V) Analyse quantifiée de la marche humaine dans la pratique hospitalière : applications aux sujets adultes âgés, Revue de médecine interne ; 20 (suppl6) :681s 11. Hornero R, Escudero J, Fernandez A, Poza J, Gomez C (2008) Spectral and nonlinear analyses of MEG background activity in patients with Alzheimer’s disease, IEEE trans. On biomedical engineering 55 (6)

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