Optimal locations and computational frameworks of FSR and IMU sensors for measuring gait abnormalities

惯性测量装置 步态 可穿戴计算机 计算机科学 计量单位 步态分析 计算机视觉 人工智能 大腿 物理医学与康复 模拟 医学 解剖 嵌入式系统 物理 量子力学
作者
Soumya K. Manna,M A Hannan Bin Azhar,Ann Greace
出处
期刊:Heliyon [Elsevier BV]
卷期号:9 (4): e15210-e15210 被引量:6
标识
DOI:10.1016/j.heliyon.2023.e15210
摘要

Neuromuscular diseases cause abnormal joint movements and drastically alter gait patterns in patients. The analysis of abnormal gait patterns can provide clinicians with an in-depth insight into implementing appropriate rehabilitation therapies. Wearable sensors are used to measure the gait patterns of neuromuscular patients due to their non-invasive and cost-efficient characteristics. FSR and IMU sensors are the most popular and efficient options. When assessing abnormal gait patterns, it is important to determine the optimal locations of FSRs and IMUs on the human body, along with their computational framework. The gait abnormalities of different types and the gait analysis systems based on IMUs and FSRs have therefore been investigated. After studying a variety of research articles, the optimal locations of the FSR and IMU sensors were determined by analysing the main pressure points under the feet and prime anatomical locations on the human body. A total of seven locations (the big toe, heel, first, third, and fifth metatarsals, as well as two close to the medial arch) can be used to measure gate cycles for normal and flat feet. It has been found that IMU sensors can be placed in four standard anatomical locations (the feet, shank, thigh, and pelvis). A section on computational analysis is included to illustrate how data from the FSR and IMU sensors are processed. Sensor data is typically sampled at 100 Hz, and wireless systems use a range of microcontrollers to capture and transmit the signals. The findings reported in this article are expected to help develop efficient and cost-effective gait analysis systems by using an optimal number of FSRs and IMUs.
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