Development of a balance context indicator during gait

Technical report

Development during gait

of a balance context indicator

J M Jullian, E P&uchon,

INSERM Unit 103, Montpellier,

P Rabischong

France

Summary This work represents the first stage in the designing of a ‘balance pattern’. This one is defined, with a balance instantaneous indicator (Bll) and a balance context indicator (BCI) taken into consideration, and from data provided by an original wearable plantar dynamics analysis system The present paper describes the features extraction method for the BCI, leading to definition of a set of descriptors. These descriptors are tested on a population of non-pathological subjects under normal and simulated abnormal gait conditions. The first results showed the feasibility of an automatic system for recognition of abnormal patterns related to dysfunction of various biomechanical components. Key words: Gait restoration, nition Gait & Posture

dynamic balance control, balance pattern, plantar forces, pattern recog-

1994; Vol2: 227-234,

December

Introduction Restoration of locomotion in paraplegics by means of electrical stimulation of motor nerves, carried out as part of the European CALIES project*, poses a major problem of dynamic control of body balance during gait. Some research teams have already implanted stimulation devices in paraplegics2. Nevertheless all tests have been undertaken in open loop without feedback from the device, the subjects controlling their own balance by means of their arms. According to the biological model, dynamic control of balance requires intervention of various control systems: visual, labyrinthic, proprioceptive3 and exteroceptive. In the latter, cutaneous plantar sensitivity appears to have a prominent role4.5. According to the physiological model, balance control requires analysis of instantaneous dynamic conditions of gait, and also the biological context in which the function is performed. While walking we are permanently aware of the state of our locomotor system, and the knowledge of fatigue level, muscle or joint states, visual conditions etc. determines the walking performance limits.

Correspondence and reprint requesfs lo: J M Jullian, INSERM Unitt 103, Appareil moteur et handicap. 395 Avenue des Moulins, 34090 Montpellier, France 0 1994 Butterworth-Heinemann 09666362l941040227-08

Ltd

These considerations suggest that the biological system generates a ‘balance pattern’ in real time. This would result from taking into account instantaneous information of balance [e.g. labyrinthic data) according to the context of the moment (state of muscles, degree of fatigue, etc.). In our methodological approach the instantaneous information of balance are synthesized into a balance instantaneous indicator (BII), and the locomotor system status at the moment is defined by a balance context indicator (BCI). The BII has to: take into account the characteristics of the very last instant of the process (physical data such as force, position, speed, acceleration, etc.). The location of these values, within dynamic tolerances depending on the system resources at the considered time, qualify the instantaneous dynamic balance; analyse these values, referring to a catalogue of predicted events, to describe the risk factor, and possibly to start a warning signal. The BCI has to: define the resources of the locomotor system at a given time (pain, biomechanical components status, i.e. actuators, joints, etc.);

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Gait & Posture 1994; 2: No 4

determine the trend by analysing changes in context and if necessary, to release a warning; determine the instantaneous tolerances for the BII in the light of the two previous items. The device capable of generating a balance pattern in real time and of deciding whether it corresponds to a normal, drifting, or alarming state, according to the functional context will constitute the ‘cybernetic interface’ (CI). This device would thus have to be defined from the highest possible number of dysfunctional cases, leading to a cognitive system that includes a catalogue of failures. Moreover the CI will have to be worn by the subject and be autonomous. To be effective such a device must be unobtrusive, light, and afford fast data processing of on-line operations. Previous physiological considerations on plantar tactile sensitivity and preceding restrictive specification of equipment led us to use an original baropodometric device for plantar dynamics analysis. Complementary discrete devices sensors such as accelerometers could also be installed on the subject if necessary. The CI will have to be learned according to a precise protocol for every subject. This protocol is developed on non-pathological subjects using various simulations. Once a paraplegic patient has received the functional stimulation device and performed the first steps ‘in open loop’ in a clinical environment, we will proceed, in the postoperatory stage, with the customization of the CI according to the previous protocol. BCI characterization Equipment for plantar dynamics analysis

The wearable system6 includes two multisensor forcesensitive insoles, each with 127 measuring points, and a portable conditioning unit. The sensor is a piezoresistive elastomer, fitted to a matrix of metallic electrodes, called artificial skin7. Each measuring point is addressed using a row/column multiplexing device. A portable microprocessor-based conditioning unit is used to scan the measuring points, and performs force data digitization and transmission. The baropodometric frames are computed from these data. The pressure data range is given at 16 linear levels. The position of the centre of forces (CF) under the right and left foot (CFr and CFl), as well as the position of the resulting centre of force (RCF), are computed from each frame. RCF represents the position of the barycentre of all forces, considering both feet side by side in a fixed configuration (Figure 1). The sampling frequency is set at 40 frames per second. Due to the variability of human walkings,y a sampling time of 25 s for one gait sequential data (1000 frames) would appear acceptable. This sequence provides us with three sets of information represented by: clusters of CFr, CFl and RCF points;

XRCF

:

l I

Figure 1. Baropodometric dynamic frame. CFr, centre of forces right; CFI, centre of forces left; RCF, resulting centre of forces.

temporal signals representing variations in the RCF abscissa (lateral position) over time; a normalized mean step (MS)6. Since the aim of this work was to characterize dynamic instability, we analysed right and left symrnetry. With the individual basic asymmetry observed under normal conditions of gait taken as reference, while correlating its variations under specific physiological configurations (joint locking, fatigue, muscle dysfunction etc.), we considered that it would be possible to generate some indicators of instability. CF cluster analysis: CF descriptors

To extract main features from the CFr, CFl (Figure 2a), and RCF (Figure 2b) clusters of points obtained for the whole gait sequence we conducted the following data preprocessing: The total insole area was divided into 20 transverse slices of equal height along the longitudinal axis, thus dividing the cluster into 20 subsets of points (Figure 2~).

1

10

20

Resulting‘t

@ Figure 2. CF clusters analysis. Reference plantar areas and CF average trajectories definition: (a) centre of forces clusters; (b) cluster of resulting centre of forces; (c) transverse slices and three reference plantar areas; (d) average trajectories

Jullian et al: Development

For each subset the coordinates of an ‘average point’ were determined with corresponding standard deviations. Average trajectories were obtained for each CFr and CFl cluster by linking the average points. The resulting average trajectory was obtained for the RCF cluster by linking the ‘resulting average points’ (Figure 2d). Finally, on each insole three plantar areas were defined corresponding to the three main functional parts of the foot generally considered by clinicians: heel, midfoot, and forefoot (Figure 2~). To analyse the right/left three different items:

symmetry

we considered

The statistical distribution of CF, reflecting the duration of solicitation of a given plantar area (temporal symmetry). The pattern analysis of CF clusters. Comparing the values of surfaces within the selected plantar areas led to quantification of the lateral scatter of points representative of the stance lateral stability; The pattern analysis of CF pathways. Comparative analysis of right and left average trajectories as well as resulting average trajectory analysis provided us with relevant information on functional symmetry. According to the above consideration following descriptors.

of a balance context indicator

229

x

xx’

@

: Longitudinal

axis of symmetly

Figure 3. RCF cluster preprocessing: RCF cluster; (b) removal of bipedal RCFI and RCFr parts to the symmetry

(a) subdividing the part; (c) shifting of axis

Sh Shid

RCFr

X’

we defined the

CFZ descriptor. Three components (CF,,-CF,,) representing the percentage of the points of RCF cluster corresponding (1) to right unipodal phase, (2) to left unipodal phase, and (3) double-stance phase. CF2 descriptor. Six components representing the percentage of the CFr and CFl within the three predefined foot areas: heel (1) right and (2) left, middle (3) right and (4) left, and forefoot (5) right and (6) left. CF3 descriptor. Six components quantifying the surface occupied by both CFR and CFL clusters over the three predefined foot areas: heel (1) right and (2) left, middle (3) right and (4) left, and forefoot (5) right and (6) left. The computation algorithm is designed as follows: the part corresponding to the cluster area in each slice is considered as the product of the standard deviation of the abscissa by the slice height. The different foot areas are obtained by adding the corresponding slice areas. CF4 descriptor. Five components, the four first representing the dimensions of the two rectangular areas in which right ( 1) longitudinal and (2) lateral, and left (3) longitudinal and (4) lateral average trajectories are inscribed. The last one (5) represents the lateral dimension of the rectangular area in which the resulting average trajectory is inscribed (Figure 2d).

Figure 4. Building of the temporal signal for a two-stride sequence: (a) rough signal; (b) adjusted signal.

(RCFl) and bipedal (RCFb) phases (Figure 3a). We laterally shifted the two RCFr and RCFl parts to the longitudinal axis of symmetry (Figure 3b and 3~). We plotted the curve that represents the RCFr and RCFl distance to the axis of symmetry over time (Figure 4a: rough signal). This temporal curve confirms the existence of a frequential message’O.” (stride frequency, physiological periodic parameters), which led us to carry out a Fourier frequential analysis. SPI descriptor. Four components representing the frequencies of (1,2) the two higher rays, (3), the rays’ amplitude ratio, and (4) the average value of the rough signal. This last value was removed before calculating the FFT so as not to alter the frequential message. Since the high amplitude value of the ray corresponding to the stride frequency was able to mask other frequential phenomena, we implemented a second temporal signal, called adjusted signal (Figure 4b), before further FFT processing. This signal was obtained by putting segments of RCF rough curves end to end. The mean value of the adjusted signal was then removed before FFT calculation. SP2 descriptor. Three components representing the values of the frequencies of (1,2) the two higher rays, of the adjusted signal and (3) their amplitude ratio.

Signal processing: SP descriptors

The RCF cluster can be divided into three parts: the part corresponding to unipodal right (RCFr), left

‘Mean step’: MS data set A mean step was calculated from each gait sequential

230

Gait & Posture 1994; 2: No 4

F

Experiments

I

I

25

50

0a

75

1’Moc

100%

I

100% of gaifcyde

2’Ma

b$ wdgM

25

50

0

75

100% ofglilqfdc

b

Figure 5. Total vertical foot/ground force amplitude variations over time during the mean step, for the (a) right, and (b) left foot, computed as percentage of bodyweight. data by linear inter/extrapolation to a normalized 40frame stride. We considered the variations over time of the total force applied under the right and left feet. According to accepted standards the force values were expressed as a percentage of body weight computed during the right and left unipodal phase of gait (Figure 5). The force/time diagrams for different subjects and gait conditions revealed two main characteristic features. First, force diagram can have several characteristic shapes. Second, we noticed temporal variations concerning some gait events. To determine and quantify these temporal differences, right and left foot diagrams were superimposed, with the foot strike time taken as the starting time. MS1 descriptor. Three components indicating (1) the average and (2) standard deviation of the stride cadence, and (3) the duration of the mean step. MS2 descriptor. Four components obtained by counting the number of frames (duration) of the four different stride phases: (1) double-support left/right, (2) right unipodal, (3) double support right/left, and (4) left. unipodal. MS3 descriptor. Two components representing the total (1) right and (2) left force ratio, and (3) the duration between right and left foot strike. MS4 descriptor. Six components representing the values of first maximum amplitude chronologically encountered for (1) the right and (2) left foot respectively, the values of second maximum amplitude for (3) the right and (4) left foot encountered chronologically (if present), and the values of the minimum amplitude between two maxima for (5) the right and (6) left foot (if present). MS5 descriptor. Six components, first maximum amplitude times from (1) left and (2) right foot strike, second maximum amplitude times from (3) left and (4) right foot strike, and intermediate minimum amplitude times from (5) left and (6) right foot strike.

To assess the validity of the predescribed indicators which could reveal functional locomotor abnormalities a series of tests were carried out on a population of non-pathological subjects. To estimate the individual basic indicator variations in normal conditions and to confirm the reliability of the analysing technique6, seven repetitive gait sequential data (175 strides equivalent) were recorded on one subject under normal conditions. Moreover, to test the possibility of identifying various deficiencies of the locomotor apparatus a set of experiments based on simulated gait dysfunctions, were conducted in the presence of the medical rehabilitation specialist: normal gait sequences; simulated gait with insufficiency of right quadriceps; simulated gait with knee joint locking; simulated gait with ankle joint locking. All gait simulations were performed using various restraining orthoses. For each subject, normal and simulated gait sequential data were repeated three times (75 strides equivalent). Subjects were asked to walk naturally, straight forward, at free cadence and speed. Data acquisition was recorded after a short period of habituation (about 15 strides equivalent), after the subject reached a ‘stable’ gait. The operator controlled the correct execution of the test by visual monitoring of plantar dynamic images. Results Reliability

The repeated tests with non-pathological subjects led to the results presented in Table 1. Overall standard deviations for the whole indicators showed marked homogeneity. However, some indicators (CF,,, CF,,, CF,,, CF,,) showed considerable variability. Normality

To achieve the study of normality a set of indicators was chosen according to variability considerations. Values of selected descriptors for five non-pathological subjects under normal gait conditions are summarized in Table 2. There were homogeneous descriptor values for any given subject, thus confirming the previous observations. Moreover, interindividual differences were demonstrated markedly for SF21_24,26, SP,,, MS,,, M&*, and more lightly for the others. Dysfunction characterization

The results of simulated perturbed gait for one subject

3 wd

2.27

8

11 10 10 8 7 5 5

3

MS1

0.31

SD

wd =withoutdimension

0.31 26

0

Mean

25

1

0.94 0.04 0.04 0.04 0.04 0.04 0.04

2

29

31 30 32 28 29 29 25

2

0.83

4

5 5 5 3 4 4 3

t%;

CF2

0.29

56

1.07 1.08 1.07 1.06 1.07 1.07 1.07

23

25 23 24 23 23 23 22 20

21 21 20 21 19 19 19

4 3 3 2 2 2 2

12

20

68

56 28 84 56 84 84 84

17 18 17 17 17 17 16

3 2 2 2 2 1 1

15 15 14 15 14 16 17

3 4 (% gait cycle)

MS2

1.76 0.88 1

4

3 4 2 4 5 5 8

456123

1.14 1.17 1.27 1.09 1.03 1.08 0.78

1 wd --

MS3

26

84

42 98 112 112 98 70 56

20 20 20 21 21 21 21

0.11 0.01

0.73 0.53 0.64 0.99 0.14

4

0.49 3

128

122 131 130 126 129 130 125

1

144

310

196 504 490 238 140 420 182

2 (%b.w)

86

452

420 336 476 602 504 350 476

tmm2)

CF3

1.01 1.07 2.57 17.00 1.8615.14 1.08 21

0.84 1.02 1.1 1.02 1.12 0.85 1.12

(S:ride.mi-I)Csf

1

27

26 27 27 29 28 27 28

1

2.75 55.88 0.04 0.94 2.06 55.30 0.94 7.03 55.71 3.07 56.28 0.94 0.94 40.88 55.95 0.94 73.83 55.59 0.94 42.67 55.66

(H3

SP2

45

44 45 44 45 46 47 47

l%?

AENI AEN AEN AEN AEN AEN AEN

1

11

47

Mean

SD

45 45 46 47 47 48 48

AENI AEN AEN AEN AEN AEN AEN

1

CFI

Table 1. Complete set of descriptorsfora non-pathological subject: repeated tests

11

142

135 140 140 150 153 155 120

2

95

582

448 616 658 658 420 644 630

5

1

60

58 60 61 60 62 60 60

16

47

31 68 62 64 39 33 32

Wur-i~

12

119

91 125 124 120 121 126 125

12

118

110 120 130 140 155 115 99

6

31

42 36 28 25 35 26 27

3 4 5 (% body-weight)

MS4

140

444

406 448 700 448 364 504 238

61

CF4

5

35

35 40 35 40 37 25 35

6

7

262

256 270 270 256 256 270 256

3

0.35

7

7 7 7 7 7 6 7

1

17

235

216 270 243 243 230 230 216

4

0.35

5

5 5 5 5 5 4 5

2

14

20

12 2 7 34 35 13 36

5

0.02

3

2.81 2.77 2.77 2.81 2.81 2.77 2.81

(H22,

8

36

47.54 28.67 47.31 27.19 31.37 32.82 36.80

zd

0.45

17

18 17 18 17 17 17 17

0.35

14

14 15 14 14 14 14 14

4 5 (% gait cycle)

MS5

0.45

19

20 19 20 19 19 19 19

3

0

1

0.94 0.94 0.94 0.94 0.94 0.94 0.94

1

SPI

0.35

12

12 13 12 12 12 12 12

6

2.64

3

1 0 3 2 6 2 8

(AmI

45 46 45

42 43 43

45 45 46

42 40 41

40 38 38

45 45

MGlN MG2N MG3N

PCIN PC2N PCBN

PLIN PLPN PL3N

TZIN TZBN

8 10

20 20 21

16 17 16

10 9 9

16 15 15

3

11 6

12 10 10

8 8 7

24 23 24

10 10 9

1

8 8

7 11

18 17 18

17 15 15

1 2 2

10 8 8

11 10 12

22 22 22

27 29 29

10 9 7

2

CF2

11 12

13 13 13

29 27 25

9 8 8

24 26 28

4

45 45 46

43 43 42

40 42 44

40 41 43

MGIN MGZN MG3N

MGlQl MGPQI MGBQI

MGlLN MGZLN MGBLN

MGlLA MGPLA MGBLA

1

47 46 44

48 47 45

46 46 47

45 46 45

CFI

21

23 22 18

12 9 9

12 11 11

13. 13 13

i3"

11 11 11

8 8 8

16 16 13

20 19 18

27 29 29

z3"

10 9 9

24

2

1

3

30 29 28

16 15 18

12 13 14

11 10 12

CF2

21 20 19 18 18 18 13 :t 11 16 19

17 :': 23 22 24 31 30 30

5

9 t

4

5

35 39

31 32 31

34 34 35

21 20 19

26 26 26

Table 3. Values of selected descriptors for one non-pathologic

47 45

40 42 41

42 43 43

42 42 42

2 (%)

EAI N EAPN EA3N

1

CFl

Table 2. Values of selected descriptors for five non-pathologic

56 53

50 50 50

56 60 60

53 53

53 47 47

53 50 50

64 56 54

42 42 40

53 47 50 67 64 56

2

1

230 230

256 256 256

243 243 230

243 243 216

243 243 243

16.34 22.89

230 243

21 20 20

22 20 20

20 20 19

19 18 17

6

47 58 54

46 43 46

44 67 60

67 64 56

1

243 243 216 202 216 216 202 202 216 189 202 202

64 56 54 49 43 54 46 47 40 38 36 38

2

CF4

202 216 216

216 216 202

216 230 230

230 243 216

4

1.36 1.35 1.28 0.96 0.87 0.90 1.00 1.01 1.02 0.83 0.81

1.14 1.11 1.20 1.16 1.11 1.09 1.20 1.23 1.24 1.13 1.06

MS1

1.14 1.11 1.20 1.18 1.19 1.18 1.25 1.26 1.23 1.19 1.18 1.16

25.63 36.48 41.14 18.78 33.41 24.39 25.49 21.66 35.14

(s”, -~-

-~

26.50 27.69 25.97

3 wd

SPI

110 157

135 142 143

112 115 115

150 146 134

0.95 1.00 0.98

1.28 1.31 1.28

1.26 1.30 1.27

1.36 1.35 1.28

Ad

117 118 125

181 141 169

132 124 125

91 126

1

114 115 108

106 107 101

107 105 105

2

140 143 140

125 139 125

1

119 117 115

MS3

0.85 0.88 0.86

:d

MS3

1.10 1.09 1.09

(s”, -_

MS1

gait perturbations

17.41 22.65 16.35

66.15 49.49 55.03

26.50 27.69 25.97

50.27 43.10 44.73

SPl ~~ 3 wd

256 216 230

176 176 176

230 243 216

189 189 189

4

subject under normal and simulated

36 35

37 41 41

29 31 32

19 18 17

24 23 24

6

CF4

subjects under normal gait conditions

136 138

134 139 134

125 129 127

204 146 172

131 115 123

140 136 123 114 121 116 104 107 109 101 113 119

144 153 135 140 109 136 101 107 107

113 113 110

136 120 141

144 162 145

191 140 162

3 4 (%I body-weight)

MS4

170 135 164

2

164 171

128 121 122

141 149 138

133 143 132

143 137 134

3 (% body-weight)

MS4

6

89 100 85

126 86 121

:z z: 96 88 94

106

100 55 95

44 59

108 112 110

105 108 101

106 58 100

124 114 0

6

84

73 62 72

5

96 100 98

53 73

101 100 103

94 100 100

78

109 108 112

5

Julian

et al: Development

of a balance

context

indicator

233

are showed in Table 3. The number and type of descriptors affected depended on the type of perturbation. Table 4 summarizes the response of indicators according to the various applied perturbations, taking the normal situation as reference. Discussion Analysis of the recordings showed that description of a gait sequence can be accurate enough to characterize the simulated perturbations used in this experiment. Nevertheless, for a given perturbation different types of pattern can be obtained depending on the subject considered, and they are detectable and reproducible for the same subject. The lack of sensitivity of some descriptors (not shown in Tables 24) was probably due to the fact that the experiments were carried out with normal young subjects who were easily able to compensate for the relatively low level of perturbation. These descriptors were not retained here, but their validity could be demonstrated when addressing pathological dysfunction. Regarding our study, the following comments can be made: the substantial number of descriptors led to a high quantity of data being taken into consideration (n dimensions system); because of differences between two normal and/or simulated gait sequence the results for some descriptors have to be considered more as a qualitative estimation of trends than as an absolute quantification of the phenomenon (noisy and fuzzy data); other descriptors led to marked differentiation of patterns, e.g. MS,,.,, which described four clearly differentiated curve shapes representing the total foot/ground force over time; technological differences between the various plantar force transducers used (homogeneity and ageing of the barosensitive elastomere) can lead to variations in sensitivity, and consequently to shifting classes. It is thus difficult to solve problems of pattern recognition using rule-based traditional methods (tolerances on values, logical AND/OR etc.). However, in our opinion the solution can be to use a system which enables: learning how to organize solutions from acquired data, generalizing, in spite of noisy and fuzzy data, learning in real time, in order to adjust the system according to environmental changes. Conclusion Despite the limited number of experiments carried out in our study, and the methodological constraints linked to the fact that only the wearable plantar dynamics analysing system was used, the first results show that human integration capabilities enable characterization of a gait context. This indicates that it should be possi-

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ble to perform automatic gait context recognitioni from a classifying system using the descriptors evaluated in this study. Such a system simulates neural connections (synapses) where information is stored and modified in real time as the system performs learning within a specific environment. This concept has some analogies with human memory that is able to attribute precise meanings to given stimulation. The use of a system such as the Nestor development system13J4 (NDS) with a development environment will enable individual testing the pertinence of each descriptor, implementing a decisional system in which each descriptor will be categorized hierarchically according to its performances. This hierarchical structure could differ according to the subject’s specifications and/or the application concerned. Designing such a classifying system will thus constitute the second stage of the research aimed at implementation of the balance context indicator. Parallel to this study we are investigating the possibility of applying this classification technique in a clinical area to discriminate between different levels of locomotor disabilities. The methodological approach remains the same, with different learning conditions and I/O features. Another application could be to follow up patients under rehabilitative treatment in order to test the efficacy of the rehabilitation process. One can assume that in the pathological context, signs will be clearly differentiated due to natural gait abnormalities, and consequently will increase the performances of the recognition system. References 1 Rabischong P, Rabischong E, Micallef JP et al. Le projet CALIES (Computed Aided Locomotion by Implanted Electra-Stimulation). In: Privat C, Herisson C ed. Recontres autour du blesse medullaire Masson, Paris, 1990 265271

2 Solomonow M. Biomechanics, electrophysiology and metabolism, of paraplegics walking with FES powered LSU reciprocating gait orthosis (RGO), Congres ISB Paris 4-8 Juillet 1993, 58-59 3 Diener HC, Dichgans J. The significance of proprioception on postural stabilization as assessed by ischemia., Brain Res 1984; 296: 103-109 4 Andre-Deshays C, Revel M. Role sensoriel de la plante

du pied dans la perception du mouvement et le contole postural. Med Chir Pied 1988; 4(4): 217-223 5 Bertoft S, Westerberg CE. The mechanoreceptors of the sole of the foot and their clinical significance. Acta Orthop Stand 1988; 59: 106111 6 Peruchon E, Jullian JM, Rabischong P. Wearable unrestraining footprint analysis system. Applications to human gait exploration. Med Biomed Eng Comput, 1989; 27: 557-565 7 Clot J, Rabischong

P, Peruchon E, Falipou J. Principal and applications of the “Artificial Sensitive Skin’. Proc. of 5th. Int. Symp. on External Control of Human Extremities, ETAN, Dubrovniik 1975, 21 l-220 8 Winter DA. Kinematic and kinetic patterns in human gait: variability and compensating effects Hum Mov SC 1984; 3: 51-76 9 Kabada MP, Ramakrishnan

HK, Wootten Me et al. Repeatability of kinematic, kinetic and electromyographic data in normal adult gait. J Orthop Res 1989; 7: 849-860 10 Capozzo A, Figura F, Leo T, Marchitti M. Symetrie et dissymetrie du pas: les composantes harmoniques comme langage pour dtchiffrer les caracteres de la marche. Eur J Biomed Technol 1979; 1, (5), 381-385 11 Antonsson EK, Mann RW. The frequency content of gait. J Biomech 1985; 18 (1): 39-47 12 Peruchon E, Jullian JM Neural nets prospects of application to pathological movement categorization. In: Woltring HJ ed. Models, Connection with Experimental Apparatus and Relevant DSP Techniques for Functional Movement Analysis. Deliverable F, Project CAMARC A

1012/AIM/DGXIII-F/CEC (Public Report), 1990, 34-39 13 Reilly D, Scofield C, Elbaum C, Cooper L. Learning system architectures composed of multiple learning modules. IEEE First International Conference on Neural Networks IL, 1987 495-503 14 Cooper L, Elbaum, Reilly D, Scofield C. Parallel multi unit, adaptative, nonlinear pattern class separator and identifier, US Patent No. 4 760 604, 26 July 1988