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Sensors and Materials, Vol. 31, No. 10 (2019) 3041–3054
MYU Tokyo
S & M 1991

Recognition of the Gait Phase Based on New Deep Learning
Algorithm Using Multisensor Information Fusion
Fangzheng Wang, Lei Yan,* and Jiang Xiao
School of Technology, Beijing Forestry University,
No. 35, Tsinghua East Road, Haidian District, Beijing 100083, China(Received June 28, 2019; accepted September 12, 2019)

Keywords: gait phase recognition, multisensor, deep learning, sensor array

Gait phase recognition is an effective method of analyzing human motion and behavior
that can be very meaningful in people’s daily life, especially when struggling with assisted
rehabilitation. In this paper, a new algorithm that can recognize a human gait phase moreaccurately is proposed. The new gait phase recognition algorithm is based on a deep memory
convolutional neural network (DM-CNN) using multiple sensor fusion. We used the plantarpressure sensor array and acceleration sensor array gait data, and then extracted the gaitfeatures using the DM-CNN. The measured data of the continuous gait cycle were divided
into unit steps, and the data were analyzed and preprocessed. Then, a feature map of eachsensor array was extracted by constructing a separate DM-CNN. Finally, each feature map was
combined into a fully connected network, and a memory function was introduced to simulate
historical behavior. We then tested the algorithm on the phases of a gait cycle and compared
the evaluation indicators of each phase. In the experiment, we compared single-mode andmultimode recognition results, and compared those with the new hidden Markov model (N-HMM),
K-nearest neighbor (KNN), and hidden Markov model (HMM) algorithms. The experimental
results show that when the multisensor data are fused, the average recognition accuracy canreach 97.1%, which is higher than those of the other algorithms and improves the recognitionof a human gait phase. The accurate recognition of human gait can provide a better theoreticalbasis for the design of exoskeleton robot control strategies.

1.

Introduction

Accurate gait phase recognition is very important to some people’s mobility, assisted
rehabilitation, and everyday lives. Research on gait recognition can be used for the control of
exoskeleton-assisted robots and smart wearable devices as well as rehabilitation for various
diseases.(1) It is very helpful for a wearer’s exercise therapy and rehabilitation to accurately
identify a human movement phase. By recognizing the gait phase and selecting appropriate
parameters to achieve safe and effective motion control, a device can act at the correct moment,
helping people work with greater precision, safety, and stability.(2)*Corresponding author: e-mail: mark_yanlei@bjfu.edu.com
https://doi.org/10.18494/SAM.2019.2493
ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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Human gait phase recognition algorithms have mainly included threshold-based methods,
time-frequency analysis, machine learning models, and combinations of these methods.
Machine learning methods have the ability to learn to capture linear and nonlinear relationships
and model them.(3)Thus far, different types of sensor, including force-sensitive resistors (FSRs), air pressure
sensors, inertial sensors, inclinometers, foot switches, and electromyography (EMG) sensors,
are readily available in the industry and can be applied to gait analysis.(4) Recent advancesin sensor technology have enabled the development of small low-cost wearable devices for awide range of applications in physiology, biomechanics, and motion data collection.(5) In thefield of gait phase recognition, sensors based on 3D optical systems exist, but have a limited
application space and can only be applied indoors or under special conditions. The EMG sensor
requires a complicated and specific mounting method that makes direct contact with the skin.
Because EMG sensors are susceptible to sweat between the skin and the sensor, this reduces theaccuracy of data collection.(6)Machine learning algorithms are widely used in the field of gait phase recognition. Ylli et al.
introduced a method of gait phase recognition using the hidden Markov model (HMM) torecognize the human motion phase, an inertial measurement unit (IMU) sensor was connectedto the leg, and a foot pressure sensor was placed on the sole of the foot.(7) Lerner et al. andTorrealba et al. used the motion data of the human leg and knee angle acquired by the IMU
sensor, and the gait phase was identified by a machine learning algorithm.(8,9) Huang et al.
used the foot pressure sensor and other techniques to obtain the body stride length.(10) The
walking pattern of stroke patients with Parkinson’s disease was analyzed using a support vector
machine (SVM), a random forest, or a mixed model. Yuan et al. used two depth cameras toobtain the trajectory of the rotation angle and the overall velocity of the selected body joint, andpathological gait was classified using the K-nearest neighbor (KNN) classifier.(11) Lim et al.
proposed a method of distinguishing gait patterns between normal and Parkinson’s disease
patients using artificial neural networks.(12) However, 37 reflective markers should be attachedduring the experiment, and data were collected using six infrared cameras, so this method islimited for use in indoor environments. Zhou et al. and Bayón et al. used the three acceleration
axes (x-, y-, and z-axes) data acquired using a smartphone to identify three walking modes using
a one-dimensional convolutional neural network algorithm.(13,14) Because the mobile phone wasplaced in the hand or pocket during data collection, the movement of the hand or pocket during
walking made it difficult to accurately measure the gait data. Altilio et al. and Aoike et al.
used the decision tree algorithm to classify the gait types of the elderly using pressure
sensors, acceleration, and gyro data.(15,16) However, because the method was classified by theexperimenter according to the set conditions, there was a problem of incorrect classification ofsome types of gait. Xin et al. used discriminant analysis algorithms and a pressure sensor toclassify gait; however, because only one pressure sensor was used, it could not accurately reflect
the characteristics of the gait phase.(17,18)
To obtain more accurate gait information and improve the recognition accuracy of the gait
phase, we propose a DM-CNN gait phase recognition algorithm in this paper. We collectedSensors and Materials, Vol. 31, No. 10 (2019)3043

motion data from 10 healthy test subjects, including plantar pressure and leg acceleration data,
and fused these data to recognize the motion phase. Then, we constructed a DM-CNN. The
method includes dividing the continuous gait data into one cycle step size, then preprocessing
the data and identifying the gait type. Features were extracted by using a DM-CNN. In the
preprocessing stage, the continuous gait data were accurately segmented according to thecharacteristics of the phase of the gait cycle. In the classification phase, we constructed multiple
features for the foot pressure and inertial sensors to extract the data feature map for each sensor.
Then, a DM-CNN was constructed to recognize the gait phase based on the feature map. The
experimental results show that when the data are fused on the basis of multiple sensors, theaverage recognition rate of the algorithm is as high as 97.1%, which can accurately recognize
the gait of the human body, improve the recognition effect of the gait stage, and provide atheoretical basis for the accurate control of the exoskeleton.
The organizational structure of the rest of this paper is as follows. In Sect. 2, we describe
the data acquisition system and experiment. In Sect. 3, we describe the DM-CNN. In Sect. 4,
we detail the results and analysis. Section 5 is the conclusion.

2.

Materials and Experiments

2.1

Acquisition system

The gait data acquisition system consists of two types of sensor, namely, acceleration andpressure sensors. The acceleration sensors acquire human leg motion data; two sensors areaffixed in the middle of the calf and two sensors in the middle of the thigh for a total of four.
Pressure sensors are used to sense the pressure on the sole of the foot. Eighteen pressure
sensors are placed on the sole of the foot. These two types of sensor can be used together toobtain accurate gait information and effectively detect the state of human motion. Figure 1
shows the data acquisition process of wearable sensors.

Fig. 1.

(Color online) Data collection using wearable sensors.

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2.1.1 Acceleration sensor
BWT901CL IMUs are used, and their performance parameters are shown in Table 1. EachIMU integrates high-precision gyroscopes, accelerometers, and geomagnetic sensors to measure
three-dimensional acceleration and three-dimensional angular velocity.(19) With a built-in
high-performance microprocessor, an advanced dynamic solution, and the Kalman filtering
algorithm, each IMU not only rapidly solves the real-time motion attitude of the module, butalso effectively reduces the measurement noise.(20)
2.1.2 Plantar pressure sensor
The pressure sensor used in the gait information acquisition system is FSR402. The greater
the pressure applied to the sensing element, the smaller the resistance of the internal circuit,resulting in a higher output voltage. The allowable pressure range for such sensors is typicallyfrom 100 g to 10 kg of gravity.(21,22) Table 2 shows the characteristics of the FSR402 pressure
sensor. To collect more plantar pressure information during the walking phase, nine pressure
sensors are affixed to each foot for a total of 18 plantar pressure sensors.
2.2

Division of gait phase

Gait is a periodic cycle from the first contact of one foot on the ground to the second contactof the heel on the ground. Usually, a gait cycle is divided into two main phases, the stance andswing phases. Heel striking the ground and the toe off the ground mark the beginning of the
stance and swing phases, respectively.(23)During the walking cycle, each leg in turn supports the person and moves him or her
forward. As shown in Fig. 2, there are four gait stages, namely, heel strike (HS), foot flat (FF),
Table 1
Specifications of BWT901CL.
Index
Parameter
Voltage (V)3.3–5
Current (mA)
<40
Volume (mm × mm × mm)
51.3 × 36 × 15
Frequency (Hz)
0.1–200

Fig. 2.

Table 2
Characteristics of the FSR402 pressure sensor.
Index
Parameter
Force range (N)
0–110
Sensing area (cm)2.54
Size [length (cm) × width (cm)] 5.68 × 3.18
Linearity error (%)
3

Distribution of foot pressure in a cycle. HS: heel strike; FF: foot flat; HO: heel off; TO: toe off.

Sensors and Materials, Vol. 31, No. 10 (2019)3045

heel off the ground (HO), and toe off the ground (TO). Initial contact is a short-lived step thatshows the interaction between the heel and the ground. During the loading response, the leg
absorbs the impact until the forefoot falls on the ground.(24) When standing in the middle, thefoot is stationary and supports the weight because the other foot begins to swing. When theheel is off the ground, the terminal posture begins and continues while the toe still touches theground. Once the toe is off the ground, the cycle enters the swing phase.(25)
2.3

Description of experiment

In the experiment, exercise data of 10 healthy test subjects were collected (age: 26 ± 2 years
old; height: 170 ± 7 cm; weight: 60 ± 8 kg; shoe size: 24.5 ± 2.5 cm). All subjects were healthy,
able to walk normally, and had no gait disturbances. Written informed consent was obtained
before the start of the experiment. Specific characteristics of the test subjects (age, height,
weight, and shoe size) are shown in Table 3. For precision, the materials of the shoes used in theexperiment were the same.
On a treadmill, each test subject was tested three times at a speed of 2.8 km/h with arecording interval of three minutes per test. In the experiment, each participant walked anaverage of 300.0 steps. All the motion data signals obtained from each participant were mixed
together to construct a larger data set to extract different motion features for training the
network model, and a total of 3000 walking cycle data were collected.
2.4

Gait data processing

Wavelet decomposition performs time-frequency transformation on the signal.(26) By
shifting and stretching, high-frequency noise can be separated from the motion gait data. Bysmoothing the processing, irregular points in the data can be eliminated, and information canbe better obtained from the data.
For plantar pressure data acquisition, nine pressure sensors were placed on each foot for a
total of 18 foot pressure sensors. For pressure sensors, the values are 0 or 1, depending on thepressure strength. A value of 0 indicates that there is no pressure, that is, the swing stage. A
value of 1 indicates a pressure value, that is, when the foot touches the ground. To divide theTable 3
Test subject characteristics.
Subject
Sex
1
Male
2
Male
3
Male
Female
4
Male
5
Male
6
Male
7
Female
8
Male
9
Male
10

Age (year)
28
26
24
27
25
26
28
27
26
27

Height (cm)
177
175
173
164
163
165
174
167
174
176

Weight (kg) Shoe size (cm)
58
26.0
60
26.0
56
26.5
22.5
52
23.5
55
24.5
54
27.0
62
23.5
64
24.5
66
23.5
68

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measured data into unit steps, we divided the length of unit steps according to the standing andswing phases of the gait cycle. Gait data samples are stored in matrix form in each pressure
sensor array and acceleration sensor array. The columns and rows of the matrix are the indicesfor each sensor measurement. Therefore, each foot pressure acquisition device consists of nine
sensors for a total of 18 columns. The acceleration sensor has a total of four columns.
To segment the gait data of each cycle, all unit steps were adjusted according to the time ofthe shortest unit step (t = 60) to standardize the length of all gaits. Therefore, the normalized
unit step measurements of the pressure and acceleration sensor arrays were converted to 60 × 18
and 60 × 12 arrays, and then stored as 1080 × 1720 × 1 vectors using the dictionary sorting
operator (X). Figure 3 shows the raw and normalized data for the unit steps.

3.

Design of Deep Memory Convolutional Neural Network (DM-CNN)

3.1

Neural network architecture

Because human gait is a continuous operation, the data collected is also continuous, leading
to the correlation between sensor measurements at each time point becoming large. Because
the convolutional neural network does not have any time concept, it only considers the current
example X and could not remember the previous data. Therefore, we propose the use of a DMCNN. By introducing a memory network to simulate historical behavior, memory encodes
information observed in the past, and it can use data correlation to recognize gait types basedon different sensor measurements. The model relies not only on the signals acquired at time t,
but also on the historical data of the signals Xd = [Xt−k, ..., Xt] to recognize current and futuregaits.
In general, DM-CNNs consist of feature extractors and fully connected networks. Eachfeature extractor consists of the following three layers: the filter, nonlinear activation function,and the feature pool layers. Figure 4 shows the overall structure of the gait phase recognition
method.
In a fully connected neural network, information flows back and forth from the lower
layer to the higher layer of the network, allowing it to learn high-level representations of theFig. 3.

(Color online) Raw and normalized data for unit steps.

Sensors and Materials, Vol. 31, No. 10 (2019)

Fig. 4.

3047(Color online) Overall structure of gait phase recognition method.

input data. A similar process has been observed in recurrent neural networks (RNNs). The
difference is that the network depends on not only the input X, but also the activation functionof the hidden unit in the previous time step. Therefore, these networks learn to map input
sequences Xd = [Xt−k, ..., Xt] and output sequences Od = [Ot+1, ..., Ot+n], as shown in Fig. 5.
According to the characteristics of RNNs, these networks have the ability to accurately predict
dense gait events. We created a neural network architecture that simulates the memory of an
RNN.
To simulate the memory of the RNN, the input tensor x is based on the following equation.
O

M =  X ( t − d ):t 

t =d

(1)

Here, X(t−d):t is a sequence of windows from beginning to end, ranging from t − d to t, where t is
the number of samples in the signal.
3.2

Network configuration

3.2.1 Input data format
The DM-CNN receives data in the form of a two-dimensional array and performs
convolution operations using various filters in the convolutional layer. The data are
preprocessed and the measurement data for each sensor array are normalized to t × W size.
These standardized data are used as inputs to the DM-CNN. W is the number of sensors in thesensor array; t is 60. For pressure and acceleration sensor arrays, the W values are 18 and 12,
respectively. To determine the number of steps required to extract the gait feature to distinguish
the gait type, a one-step data sample is used for the classification experiment. The gait data
sample of a cycle is defined as one step, and the input data size of the DM-CNN is (t × 1) × W.

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Fig. 5. (Color online) Information flow of the deep memory neural network. The left figure shows the RNN
infinite loop network with the output of the model as input feedback. The right figure is the unfolded representationof an RNN.

3.2.2 Convolution layer
The DM-CNN used in this method consists of three convolutional layers. Figure 6 shows
the structure of the single DM-CNN for each sensor array. Each layer contains filters for thecorresponding functional level. For each layer of convolution, the following three parameters
are determined: the number of filters, f, the filter size w × h, and the span s. In the first
convolutional layer, 32 filters are used, and the size of the filter is set to w × 20. The DMCNN input data are one cycle of gait data. The second and third convolution layers use 64 and128 types of filter, respectively. All filters operate at a location in the input data to generate afeature vector.
The DM-CNN is trained using a back-propagation algorithm. In the learning process,when the number of neural network layers increases, the weight difference in the error backpropagation is close to zero. To prevent this phenomenon, we used the rectified linear unit (ReLU)
as the activation function. To prevent the internal covariance shift caused by the nonlinear
activation function used in each layer of the neural network, applying batch normalization after
the batch activation function enhances learning stability.
3.2.3 Fully connected network and output
Each feature map is converted into a DM-CNN feature vector (flattened) as an input to a
fully connected network. The output layer consists of the same number of nodes as the class
to be identified. On each node, the weights of the previous layer are applied to the SoftMax
function to calculate the final output. In this paper, single- and multimode DM-CNNs areconstructed according to the number of sensor arrays. In the single-mode DM-CNN, the feature
vectors extracted from a sensor array are employed as an input to a fully connected network. In
the multimode DM-CNN, the feature vectors of the two sensor arrays are connected together as
an input to a fully connected network. Figure 7 shows the structure of two mode networks thatuse single- and multimode DM-CNN feature vectors as inputs to determine the gait phase.

Sensors and Materials, Vol. 31, No. 10 (2019)

Fig. 6.

3049(Color online) Structure of the DM-CNN for each sensor array.

Fig. 7. (Color online) Fully connected network structure that uses single- and multimode DM-CNN feature
vectors as inputs to determine gait phase.

3.3

Evaluation indicators

To test the recognition of a human gait phase based on the DM-CNN algorithm, we selected
the precision, recall rate, and accuracy indicators to evaluate gait recognition performance. The
precision indicator considers the impact of the sample distribution on the detection rate. The
recall rate indicator refers to the proportion of positive samples correctly identified to the total
number of positive samples, which is regarded as the detection rate of a single gait stage.(27) The
accuracy indicator is the result of positive samples detected by the classifier and the proportionof real positive samples. The calculation formulas are as follows:

TP,
TP + FP(2)

TP,
TP + FN(3)

TP + TN.
TP + TN + FP + FN

(4)Precision: P =Recall: R =Accuracy: A =3050

Sensors and Materials, Vol. 31, No. 10 (2019)4.

Results

4.1

Results and analysis: Part 1

To evaluate the recognition performance of single- and multimode data fusion based on the
DM-CNN algorithm, 1500 samples were randomly extracted from the data set to establish a
training and test set, 1000 samples were selected for the training set, and 500 samples for thetest set. To increase the statistical confidence, the above procedure was repeated 20 times tocalculate the average recognition accuracy, as shown in Figs. 8(a) and 9(a). We also performed
a k-fold cross validation experiment, as shown in Figs. 8(b) and 9(b). For the k-fold crossvalidation, the total training samples were randomly divided into k equal-sized subsets. In the
k subset, a separate subset was retained as the validation set for the validation model, while the

(a)

(b)

Fig. 8. (Color online) Average accuracy of DM-CNN phase recognition for single mode. (a) Randomly selected1000 training samples and 500 test samples; (b) DM-CMM recognition rate for single-mode 4-fold cross validation.

(a)

(b)

Fig. 9. (Color online) Average accuracy of DM-CNN phase identification for multimode. (a) Randomly selected1000 training data samples and 500 test set data samples; (b) 4-fold cross validation.

Sensors and Materials, Vol. 31, No. 10 (2019)3051

remaining k−1 subsets were used as the training set. Since k is usually set to the number ofclasses, we performed a 4-fold cross validation in the experiment.
The recognition performance was evaluated using a single-mode DM-CNN experimentof a sensor array data learning network and a multimode DM-CNN experiment using adata learning network of two sensor arrays. The single mode refers to the recognition result
obtained using a sensor and the multimode refers to the result obtained using two sensor arrays
together. Figure 8 shows the results of gait phase recognition using a single-mode DM-CNN,
which trains each sensor array independently. In Fig. 8(a), the average recognition performanceof the single-mode acceleration sensor is slightly higher than that of the pressure sensor array.
The gait information generated by acceleration motion can be considered better for gait phaserecognition. Figure 8(b) shows the results of gait phase average recognition based on 4-foldcross validation using 1000 training and 500 test samples. The average recognition rate is
between 85 and 90%, and the gait recognition rate is higher than that shown in Fig. 8(a).
Figure 9 shows experimental results for single- and multimode DM-CNNs. To clearly show
the effectiveness of the method, we compared it with that of Sec et al.’s method, the latest
method using smart insoles.(28) Since Sec et al.’s method uses only pressure sensors, we firstcompared a single-mode DM-CNN with Sec et al.’s method, and the average recognition ratesof single-mode DM-CNNs were 6% higher than those of Sec et al.’s method. At the same time,a multimode DM-CNN was compared with a single-mode DM-CNN, its average recognition
rate is 8% higher than that of the single mode. The results in Fig. 9(a) show that the multimode
DM-CNN has a higher gait recognition than the single-mode DM-CNN. In the 4-fold crossvalidation experiment, as shown in Fig. 9(b), the multimode DM-CNN gait phase average
recognition accuracy of the 4-fold cross validation is as high as 97.1%, and the average accuracy
is 7.9% higher than that without cross validation.
In Figs. 8 and 9, the experimental results show that using two types of sensor array (multimode)
can achieve a higher recognition performance than using only one type of sensor array.
Moreover, the performance of the multimode DM-CNN gait phase recognition with 4-fold crossvalidation is more accurate.
4.2

Results and analysis: Part 2

To evaluate the effectiveness of the algorithm in gait phase recognition, we compared therecognition effects of the HMM, K-KNN, and new hidden Markov model (N-HMM) algorithms
applied in the gait phase and analyzed their performance characteristics. The acceleration
signals of human leg and foot pressure data were used for training. These data were derived
from the movement data of 10 test subjects on a treadmill. Using the same training set and test
set data, the gait stage recognition model based on the DM-CNN, HMM, N-HMM, and KNN
was used in the 4-fold cross validation. The trained model was used to identify the test data andcompare the gait recognition results of the HMM, N-HMM, and KNN. The precision, recall
rate, and average recognition accuracy are shown in Table 4 and Fig. 10.
As shown in Fig. 10(a), the DM-CNN is higher in the recall indicators of HS, FF, HO, andTO than the N-HMM, HMM, and KNN in the gait stage, which shows that the DM-CNN has

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Table 4Comparison of average recognition accuracy of algorithms.
Algorithm
HS
FF
HO
DM-CNN
97.2%
97.1%
96.8%
N-HMM
96.5%
96.3%
95.4%
HMM
92.3%
93.4%
89.5%
KNN
90.2%
85.6%
86.5%

(a)

TO
97.3%
96.6%
94.2%
93.1%

Average accuracy
97.1%
96.2%
92.3%
88.5%

(b)

Fig. 10. (Color online) Evaluation results of different algorithms: (a) evaluation of recall rate experimental results; (b)evaluation of precision rate experimental results.

a better recognition effect for these stages. The average recall rate of the DM-CNN is 94.5%,
whereas that of the N-HMM is 93.5%, that of the HMM is 90.5%, and that of the KNN is82.5%. Figure 10(b) shows that the average precision rate of the DM-CNN is 95.9%, whereasthat of the N-HMM is 95.5%, that of the HMM is 92%, and that of the KNN is 81.5%. Fromthese results, we can also conclude that the DM-CNN has a better gait phase recognition thanthe N-HMM, HMM, and KNN. Table 4 shows that the average recognition accuracy rate of theDM-CNN is 97.1%, that of the N-HMM is 96.2%, that of the HMM is 92.3%, and that of the
KNN is 88.5%. Therefore, the experimental results show that the DM-CNN proposed in thispaper has a higher gait phase recognition effect than other algorithms.

5.

Conclusion

Gait phase recognition plays an important role in motion analysis, identity recognition,and other fields. In this paper, a gait recognition algorithm based on a DM-CNN is proposed.
The algorithm uses the fusion of human body motion acceleration and plantar pressure data
to extract features from the fused data and accurately identify the gait phase. Feature maps
were extracted from the preprocessed data, and each feature map was independently learned
for each sensor array using a DM-CNN. We combined these feature maps and used them as
inputs to a fully connected network to recognize gait phases. Data from 10 healthy test subjects
were collected for recognition experiments. The 4-fold cross validation method was used. It

Sensors and Materials, Vol. 31, No. 10 (2019)3053

was found that the recognition performance of the multimode DM-CNN with the two sensor
arrays is higher than that of the single-mode DM-CNN with one sensor array. Furthermore, therecognition performance of the multimode DM-CNN was compared with those of the N-HMM,
HMM, and KNN. The experimental results show that the precision, recall rate, and accuracy of
the DM-CNN based on multi-sensor fusion are higher than those of the other algorithms. The
average recognition accuracy of the DM-CNN is 97.1%, which is higher by 0.9, 4.8, and 8.6%.
The proposed algorithm can recognize human gait more effectively than other algorithms,
which helps lay the theoretical foundation for the development of smart wearable robots.
In future work, researchers need to study the phases of different motion conditions, such as
walking, climbing stairs, and uphill and downhill slopes. In addition, more work needs to bedone to determine whether more sensors will be needed to recognize the phase under different
motion conditions.

Acknowledgments
This work was financially supported by the Fundamental Research Funds for the Central
Universities (No. 2015ZCQ- GX-03). The authors declare that there is no conflict of interest
regarding the publication of this paper.

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