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Wearable-Gait-Analysis-Based Activity Recognition: A Review

Stella Ansah

Stella Ansah

Department of Electrical and Computer Engineering, University of New HampshireDurham,
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Ansah, Stella
 und 
Diliang Chen

Diliang Chen

Department of Electrical and Computer Engineering, University of New HampshireDurham,
Suche nach diesem Autor auf
Sciendo|Google Scholar
Chen, Diliang
  
04. Jan. 2023
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Artikel-Kategorie: Article
Online veröffentlicht: 04. Jan. 2023
Eingereicht: 31. März 2022
DOI: https://doi.org/10.2478/ijssis-2022-0021
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© 2022 Stella Ansah et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1

Four main steps for WGA-based activity recognition. The pressure sensors and IMU in the “Data Collection” section represent the commonly used wearable sensors in WGA-based activity recognition systems. The plots in the “Data Segmentation” section represent the gait cycle-based method which involves the segmentation of data through the detection of gait cycles and, the fixed non-overlapping sliding window approach which involves the segmentation of data using fixed time windows. To extract features for activity recognition, knowledge-driven features and data-driven features are frequently used. The icons in the “Classification” section represent examples of activities that can be recognized by activity recognition systems during the classification phase.
Four main steps for WGA-based activity recognition. The pressure sensors and IMU in the “Data Collection” section represent the commonly used wearable sensors in WGA-based activity recognition systems. The plots in the “Data Segmentation” section represent the gait cycle-based method which involves the segmentation of data through the detection of gait cycles and, the fixed non-overlapping sliding window approach which involves the segmentation of data using fixed time windows. To extract features for activity recognition, knowledge-driven features and data-driven features are frequently used. The icons in the “Classification” section represent examples of activities that can be recognized by activity recognition systems during the classification phase.

Figure 2

Flow Chart of the Article Selection Process.
Flow Chart of the Article Selection Process.

Figure 3

Distribution of the WGA-Based Activity Recognition Publications Over Time.
Distribution of the WGA-Based Activity Recognition Publications Over Time.

Figure 4

Commonly used IMU sensor positions. a) Three IMU sensors positioned at the thigh, shank, and foot to capture data for activity recognition [32]. b) A single IMU sensor worn at the ankle for activity recognition [29].
Commonly used IMU sensor positions. a) Three IMU sensors positioned at the thigh, shank, and foot to capture data for activity recognition [32]. b) A single IMU sensor worn at the ankle for activity recognition [29].

Figure 5

Different numbers and locations of pressure sensors used in WGA-based activity recognition systems. a) A pressure sensor array with 96 pressure sensors evenly distributed on it [14]. b) Eight pressure sensors distributed at the big toe, metatarsal, and heel [23, 59]. c) Five pressure sensors placed at the toe, metatarsal, and heel [34].
Different numbers and locations of pressure sensors used in WGA-based activity recognition systems. a) A pressure sensor array with 96 pressure sensors evenly distributed on it [14]. b) Eight pressure sensors distributed at the big toe, metatarsal, and heel [23, 59]. c) Five pressure sensors placed at the toe, metatarsal, and heel [34].

Figure 6

Foot contact pitch during (a) walking, (b) stair ascent, (c) stair descent, and (d) the double float phase during running. This gait-analysis-based parameter was used by Chen et al. [14] in the recognition of activities.
Foot contact pitch during (a) walking, (b) stair ascent, (c) stair descent, and (d) the double float phase during running. This gait-analysis-based parameter was used by Chen et al. [14] in the recognition of activities.

Figure 7

Other wearable sensor types which can be employed in activity recognition. a) Barometer [1] b) Strain sensor [28].
Other wearable sensor types which can be employed in activity recognition. a) Barometer [1] b) Strain sensor [28].

Summary of WGA-based Activity Récognition Techniques

Références Recognized Activities Wearable Sensors Data Segmentation Extracted Features Activity Recognition
Martinez et al. [32] Level-ground walking, ramp ascent, and ramp descent. 3-axis gyroscope and pressure sensors. Gait cycle-based method Time-domain features Adaptive Bayesian Inference method
McCalmont et al. [35] Slow walking, normal walking, Fast walking, stair ascent, and stair descent. 3-axis accelerometer, 3-axis gyroscope, 3-axis magnetometer, Pressure sensor array. Gait cycle-based method Time-domain features and gait-based features. Artificial neural network, K-nearest neighbour (KNN), and Random Forest.
Ng et al. [42] Walking, sitting, lying, and falling. Sensor tags Gait cycle-based method Raw sensor data KNN and Random
Lopez et al. [29] Level-ground walking, Stair ascent, stair descent, Ramp ascent, and ramp descent. 3-axis accelerometer Gait cycle-based method. Time-domain features and frequency-domain features. KNN
Chenet al. [14] Walking, running, standing, sitting, stair ascent, and Stair descent. 3-axis accelerometer, 3-axis gyroscope, Pressure sensor array. Gait cycle-based method Gait-based features Support vector machine (SVM)
Jeong et al. [23] Level-ground walking, ascent. and stair descent. Pressure sensors Gait cycle-based method Raw sensor data SVM
Truong et al. [59] Level-ground walking, stair ascent. and stair descent. Pressure sensors Gait cycle-based method Time-domain features SVM
Martinez et al. [33] Level-ground walking, ramp ascent, and ramp descent. 3-axis accelerometer, 3-axis gyroscope, and Pressure sensors. Gait cycle-based method Time-domain features Bayesian formulation Based approach
Achkaretal. [38] Level-ground walking, standing, sitting, stair ascent, stair descent, Ramp ascent, and ramp descent. 3-axis accelerometer, 3-axis gyroscope, 3-axis magnetometer, Pressure sensors, and barometric sensor. Gait cycle-based method Gait-based features. Rule-based method.
Zhao et al. [66] Level-ground walking, Stair ascent. stair descent. Ramp ascent, and ramp descent. Pressure sensors and electromyography sensors. Gait cycle-based method Time-domain features. SVM
Mazumder et al. [34] Level-ground walking, fast walking, standing, sitting, Stair ascent, stair descent, and ramp ascent. 3-axis accelerometer, 3-xis gyroscope, and pressure sensors. Gait cycle-based method Time-domain features, Polynomial coefficients Extracted from hip angle Trajectory and centre-of-pressure (CoP) trajectory. SVM
Camargo et al. [10] Level-ground walking, Stair ascent, stair descent, Ramp ascent, and ramp descent. 3-axis accelerometer, 3-axis gyroscope, goniometer, and îlectromyography sensor. Gait cycle-based method Time-domain features and frequency-domain features. Dynamic Bayesian network
Ershadi et al. [20] Toe level ground walking, Normal level-ground walking, Sitting, and standing. Pressure sensors. Gait cycle-based method Time-domain features. Rule based method
Martindale et al. [31] Level-ground walking, sitting, stair ascent, stair descent, jogging, running, cycling, and jumping. 3-axis accelerometer, 3-axis gyroscope, and pressure sensors. Gait cycle-based method Raw sensor data. Convolutional Neural Networks (CNN) and Récurrent Neural Network (RNN).
Benson et al. [8] Normal running and fast running. 3-axis accelerometer, 3-axis gyroscope Gait cycle-based method Time-domain features, frequency-domain features, and wavelet-based features. SVM
Hamdi et al. [22] Level-ground walking, Stair ascent, stair descent, ramp ascent, and ramp descent. 3-axis accelerometer, and 3-axis gyroscope Gait cycle-based method Gait-based features, time-domain features, frequency-domain, and wavelet-based features. Random Forest
Achkar et al. [39] Level-ground walking, standing, sitting, Stair ascent, stair descent, Ramp ascent, and ramp descent. 3-axis accelerometer, 3-axis gyroscope, 3-axis magnetometer, pressure sensors, and barometric sensor. Gait cycle-based method Gait-based features and time-domain features. Rule based method
Xiuhua et al. [27] Level-ground walking, Ramp ascent, and ramp descent. 3-axis accelerometer, 3-axis gyroscope, and pressure sensors. Gait cycle-based method Gait-based features. Class incrémental learning method.
Ngo et al. [2] Level-ground walking, Stair ascent, stair descent, Ramp ascent, and ramp descent. 3-axis accelerometer and 3-axis gyroscope. Gait cycle-based method Time-domain features. KNN and SVM.
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