Wearable sensor-based pattern mining for human activity recognition: deep learning approach

Abstract

Purpose This paper aims to deal with the human activity recognition using human gait pattern. The paper has considered the experiment results of seven different activities: normal walk, jogging, walking on toe, walking on heel, upstairs, downstairs and sit-ups. Design/methodology/approach In this current research, the data is collected for different activities using tri-axial inertial measurement unit (IMU) sensor enabled with three-axis accelerometer to capture the spatial data, three-axis gyroscopes to capture the orientation around axis and 3° magnetometer. It was wirelessly connected to the receiver. The IMU sensor is placed at the centre of mass position of each subject. The data is collected for 30 subjects including 11 females and 19 males of different age groups between 10 and 45 years. The captured data is pre-processed using different filters and cubic spline techniques. After processing, the data are labelled into seven activities. For data acquisition, a Python-based GUI has been designed to analyse and display the processed data. The data is further classified using four different deep learning model: deep neural network, bidirectional-long short-term memory (BLSTM), convolution neural network (CNN) and CNN-LSTM. The model classification accuracy of different classifiers is reported to be 58%, 84%, 86% and 90%. Findings The activities recognition using gait was obtained in an open environment. All data is collected using an IMU sensor enabled with gyroscope, accelerometer and magnetometer in both offline and real-time activity recognition using gait. Both sensors showed their usefulness in empirical capability to capture a precised data during all seven activities. The inverse kinematics algorithm is solved to calculate the joint angle from spatial data for all six joints hip, knee, ankle of left and right leg. Practical implications This work helps to recognize the walking activity using gait pattern analysis. Further, it helps to understand the different joint angle patterns during different activities. A system is designed for real-time analysis of human walking activity using gait. A standalone real-time system has been designed and realized for analysis of these seven different activities. Originality/value The data is collected through IMU sensors for seven activities with equal timestamp without noise and data loss using wirelessly. The setup is useful for the data collection in an open environment outside the laboratory environment for activity recognition. The paper also presents the analysis of all seven different activity trajectories patterns.

PROMPT: Process Mining and Paravector Tensor-Based Physical Health Monitoring Framework

“…The process model was generated using flow patterns based on sensors, time, and users in a smart environment. Another study [16] used the pattern mining approach to model user behavior using gait pattern analysis. The study used various filters and cubic spline techniques to analyze the user's gait to recognize high-level activities.…”

Human activity recognition based on multi‐instance learning

“…Joint angle calculations can increase the adoption of wearable sensors in the field of HAR and improve the performance of the predictive model. Recent advancements in the calculation of the joint angles of the hip, knee, and ankles have motivated us to develop a reliable and robust model for gait activity recognition using wearable sensors (Bijalwan et al, 2022).…”

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

“…In recent times, deep learning automatically provides high‐level feature extraction for achieving high performance in various fields. HAR (Bijalwan & Semwal & Gupta, 2021) system grabs the attention as a new framework. Various researchers have concentrated on the implementation of activity approaches.…”