FSR and IMU sensors-based human gait phase detection and its correlation with EMG signal for different terrain walk

Abstract

Purpose The purpose of this paper is to present gait analysis for five different terrains: level ground, ramp ascent, ramp descent, stair ascent and stair descent. Design/methodology/approach Gait analysis has been carried out using a combination of the following sensors: force-sensitive resistor (FSR) sensors fabricated in foot insole to sense foot pressure, a gyroscopic sensor to detect the angular velocity of the shank and MyoWare electromyographic muscle sensors to detect muscle’s activities. All these sensors were integrated around the Arduino nano controller board for signal acquisition and conditioning purposes. In the present scheme, the muscle activities were obtained from the tibialis anterior and medial gastrocnemius muscles using electromyography (EMG) electrodes, and the acquired EMG signals were correlated with the simultaneously attained signals from the FSR and gyroscope sensors. The nRF24L01+ transceivers were used to transfer the acquired data wirelessly to the computer for further analysis. For the acquisition of sensor data, a Python-based graphical user interface has been designed to analyze and display the processed data. In the present paper, the authors got motivated to design and develop a reliable real-time gait phase detection technique that can be used later in designing a control scheme for the powered ankle-foot prosthesis. Findings The effectiveness of the gait phase detection was obtained in an open environment. Both off-line and real-time gait events and gait phase detections were accomplished for the FSR and gyroscopic sensors. Both sensors showed their usefulness for detecting the gait events in real-time, i.e. within 10 ms. The heuristic rules and a zero-crossing based-algorithm for the shank angular rate correctly identified all the gait events for the locomotion in all five terrains. Practical implications This study leads to an understanding of human gait analysis for different types of terrains. A real-time standalone system has been designed and realized, which may find application in the design and development of ankle-foot prosthesis having real-time control feature for the above five terrains. Originality/value The noise-free data from three sensors were collected in the same time frame from both legs using a wireless sensor network between two transmitters and a single receiver. Unlike the data collection using a treadmill in a laboratory environment, this setup is useful for gait analysis in an open environment for different terrains.

Detection and assessment of Parkinson's disease based on gait analysis: A survey

“…For instance, Mazilu et al ( 2013 ) construted the CuPiD dataset by collecting gait data of PD patients with multiple wearable sensors, including IMU sensors attached to different body parts, a smartphone in the pocket, pressure insoles, chest-mounted ECG and head-mounted fNIR. Negi et al ( 2021 ) implemented the fusion of pressure insole, IMU and EMG signals for analyzing different terrain walk. Using IMU and EMG, Celik et al ( 2022 ) introduced a novel data fusion algorithm for enabling gait analysis for both clinical and free-living assessments.…”

Design and analysis of magnetorheological damper based ankle-foot prosthesis prototype

“…A single damper attached to a prosthetic foot can provide able-bodied gait kinematics to below-knee amputees with promising real-time gait phase detection modality, driving circuitry and a control system. In the previous study, the authors have presented real-time gait phase detection using FSR and IMU sensors (Negi et al , 2021). FSR and IMU sensors provide necessary information for normal gait kinematics; also, the EMG signal indicates the muscle activation at different gait phases, thereby enabling the control of the MR damper's current during the different phases of a gait cycle.…”

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“…Promising results were also revealed by the EEG and EMG hybrid modality [85], [24], [55], [42], [87]. Chung et al [44] focused on the effects of age and gender.…”