Pattern identification of different human joints for different human walking styles using inertial measurement unit (IMU) sensor

Semwal, Vijay Bhaskar; Gaud, Neha; Lalwani, Praveen; Bijalwan, Vishwanath; Alok, Abhay Kumar

doi:10.1007/s10462-021-09979-x

Cited by 130 publications

(47 citation statements)

References 59 publications

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“…In Semwal, Gaud, et al (2021), to perform the walking task, human gait data of six different walking styles named brisk walk, normal walk, very slow walk, medium walk, jogging, and the fast walk was collected through the configured IMU sensor and mobile‐based accelerometers device. To capture the pattern for six different walking styles, data was extracted from the hip, knee, ankle, shank, thigh, and foot area.…”

Section: Literature Surveymentioning

confidence: 99%

Human activity recognition by wireless body area networks through multi‐objective feature selection with deep learning

Boga

Kumar

2022

Expert Systems

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Wireless body area network (WBAN) is a novel technology with the incorporation of numerous types of devices, which is also employed in health monitoring applications.Human activity recognition (HAR) receives more interest in recent times along with wearable sensors. HAR system provides information about a person's identity, personality, and psychological state. In the present scenario, it becomes important to model the active learning paradigms with the help of wearable sensors for analysing human activities. Although various deep learning models and existing algorithms secure better outcomes through the sensor data analysis regarding HAR, the decision-making evaluation seems to be a complex one. The main intent of this paper is to implement the WBAN-based HAR system using the improved deep learning model. By connecting the wearable sensors, the signals are gathered regarding human activity from diverse benchmark sources. After collecting the required signals, the pre-processing of the input signals is done using artefact removal and median filtering. Further, the feature extraction is performed, which intends to extract a set of features by utilizing short-time Fourier transform (STFT) and statistical features. For reducing the feature-length, a multi-objective-based optimal feature selection is adopted. Human activities such as 'walking, walking upstairs, walking downstairs, sitting, standing, lying, and jogging' are recognized with help of selected optimal features. The optimized probabilistic neural network (PNN) and convolutional neural network (CNN) are combined and named as adaptive probabilistic-based CNN (AP-CNN). The effective performance of optimal feature selection and recognition is accomplished by incorporating the developed backward updating position-based sea lion optimization algorithm (BU-SLnO). Finally, the performance of the BU-SLnO-AP-CNN-based suggested model is analysed, which shows 1.001%, 1.237%, 0.811%, and 0.859% advanced than SLnO-AP-CNN, feed-forward-AP-CNN (FF-AP-CNN), grey wolf optimization-AP-CNN (GWO-AP-CNN), particle swarm optimization-AP-CNN (PSO-AP-CNN) when observing the dataset 1. The experimental outcomes from comparison with various classification techniques demonstrate the efficiency of the developed technique.

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Section: Literature Surveymentioning

confidence: 99%

Human activity recognition by wireless body area networks through multi‐objective feature selection with deep learning

Boga

Kumar

2022

Expert Systems

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“…With few limitations and vast unexplored potential, Deep learning has become a top choice for researchers in different fields of study. Applications of deep learning and wearable technology can be extended to lower extremity recognition tasks from monitoring patients in clinical or home environments (Semwal, Gaud, et al, 2021), monitoring newborns in maternity clinics to measuring performance metrics of athletes and recovery rate of individuals in rehabilitation centres (Semwal, Mazumdar, et al, 2021). A robust and reliable technology in the area of human activity recognition, be it personalized healthcare, robust authentication mechanism, prosthesis, biped robotics (Semwal & Nandi, 2017), etc.…”

Section: Introductionmentioning

confidence: 99%

Deep ensemble learning approach for lower extremity activities recognition using wearable sensors

2021

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Human walking is a very challenging task and always requires rigorous practice. It is a learning process that involves the complex coordination of the brain and lower limbs. The bipedal robots that mimic the human morphological structure to produce human similar walking, are not capable of producing an efficient walk. Due to walking challenges and structural differences, a robot cannot walk like a human being. In this research, to achieve the aforementioned objective to produce a human similar walk, human lower extremity activities are considered to understand walking behaviour. The experiment involves different walking styles on different terrains. To capture the learning process of bipedal robot locomotion, a deep learning‐based ensemble classifier is introduced for human lower activities recognition. To understand the learning process seven different walking activities are considered for analysis purposes. An Inertial measurement unit (IMU) is used as a wearable device due to its small form factor and unobtrusive nature to capture the walking movement of different lower limbs joints. Three public datasets viz. mHealth, OU‐ISIR similar action and HAPT inertial sensor data sets are considered for this study. To classify the activities, 2 different deep learning models namely convolutional neural network (CNN) and long short‐term memory (LSTM) are used. To generalize the results, an ensemble of different classifiers is implemented. The Classifier has reported accuracy of 99.25%, 88.48% and 97.44%, respectively, on the aforementioned data sets. This work can be utilized for elderly subjects' postural stability, rehabilitation of patients post‐stroke and trauma, generation of robot walk trajectories in cluttered environment and reconstruction of impaired walking.

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“…In normal conditions when a person performs a walk, his stance is upright and the center of gravity of his body tries to stable his movement (Mazumdar & Jha, 2020). Also during walking, a person swings his arms on the sides which also helps to stabilize and coordinate his motion (Semwal, Gaud, et al, 2021). A person's gait pattern depends on many factors like age, sex, personality, mood, and health condition (Semwal et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous computing model for post‐injury walking pattern restoration and postural stability rehabilitation exercise recognition

Bijalwan¹,

Semwal

Singh

et al. 2021

Expert Systems

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The research paper presents the heterogeneous computing model for analysis & restoration of human walking deformity and posture instability. Gait‐related walking activities are very important for the analysis of postural instability, repairment of gait abnormality, diagnosis of cognitive declination, enhance the cognitive ability of human‐centered humanoid robot system, and many clinical diagnoses, for example, Parkinson, pathological gait, freezing of gait, etc. at an early stage. For experiment analysis, 10 different lower limb activities are being considered of healthy and crouch walking subjects. A total of 25 healthy and 10 crouch walk subjects are considered for experiment purposes of different age groups, sex, and mental status. To achieve this objective the pattern of 10 different rehabilitation activities are captured using RGB‐Depth (RGB‐D) camera and classified using heterogeneous deep learning models. Different deep learning models Convolutional Neural Network (CNN) and CNN‐LSTM (CNN‐Long Short Term Memory) are used for the classification of these rehabilitation exercises. The RGB‐D data is obtained using a Microsoft Kinect v2 sensor on a 100 Hz sampling frequency. Experimental results have shown significant activity recognition accuracy with 96% and 98% for CNN and CNN‐LSTM models respectively.

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Pattern identification of different human joints for different human walking styles using inertial measurement unit (IMU) sensor

Cited by 130 publications

References 59 publications

Human activity recognition by wireless body area networks through multi‐objective feature selection with deep learning

Human activity recognition by wireless body area networks through multi‐objective feature selection with deep learning

Deep ensemble learning approach for lower extremity activities recognition using wearable sensors

Heterogeneous computing model for post‐injury walking pattern restoration and postural stability rehabilitation exercise recognition

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