Abstract
A three‐dimensional motion capture system is a useful tool for analysing gait patterns during walking or exercising, and it is frequently applied in biomechanical studies. However, most of them are expensive. This study designs a low‐cost gait detection system with high accuracy and reliability that is an alternative method/equipment in the gait detection field to the most widely used commercial system, the virtual user concept (Vicon) system. The proposed system integrates mass‐produced low‐cost sensors/chips in a compact size to collect kinematic data. Furthermore, an x86 mini personal computer (PC) running at 100 Hz classifies motion data in real‐time. To guarantee gait detection accuracy, the embedded gait detection algorithm adopts a multilayer perceptron (MLP) model and a rule‐based calibration filter to classify kinematic data into five distinct gait events: heel‐strike, foot‐flat, heel‐off, toe‐off, and initial‐swing. To evaluate performance, volunteers are requested to walk on the treadmill at a regular walking speed of 4.2 km/h while kinematic data are recorded by a low‐cost system and a Vicon system simultaneously. The gait detection accuracy and relative time error are estimated by comparing the classified gait events in the study with the Vicon system as a reference. The results show that the proposed system obtains a high accuracy of 99.66% with a smaller time error (32 ms), demonstrating that it performs similarly to the Vicon system in the gait detection field.
Highlights
Since its development in the 1970s, modern gait identification and analysis has been widely used in walking rehabilitation, gait training, life assistance, and motion monitoring [1,2,3,4]
The gait detection system is widely applied in the exoskeleton since it is capable of identifying walking patterns [5, 6]
This study mainly focuses on time-domain feature extractions as the sampling frequency of the Uno R3 board is too low for Fourier transform
Summary
Since its development in the 1970s, modern gait identification and analysis has been widely used in walking rehabilitation, gait training, life assistance, and motion monitoring [1,2,3,4]. The gait detection system is widely applied in the exoskeleton since it is capable of identifying walking patterns [5, 6]. Three-dimensional motion analysis (3DMA) is the gold standard for biomechanical analysis since it records the most accurate kinematic data [7]. The camera-based two-dimension analysis is an alternative method to detect movement and is proven to work effectively in gait analysis in various environments [8]. Video recording generated a vast amount of data and required a high computing performance [9]. These drawbacks limited the use of two-dimensional visual analysis in gait analysis
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