Open source platform for collaborative construction of wearable sensor datasets for human motion analysis and an application for gait analysis

An Open Data Set of Inertial, Magnetic, Foot-Ground Contact, and Electromyographic Signals From Wearable Sensors During Walking

This study describes an open data set of inertial, magnetic, foot-ground contact, and electromyographic signals from wearable sensors during walking at different speeds. These data were acquired from 22 healthy adults using wearable sensors and walking at self-selected comfortable, fast and slow speeds, and standing still. All data are publicly available in the Internet (https://doi.org/10.6084/m9.figshare.7778255). In total, there are data of 9,661 gait strides. This data set also contains files with the instants of the gait events identified using the foot-ground contact sensors and notebooks exemplifying how to access and visualize the data. This data set gives the opportunity to all interested researchers to work with such data, for example, making tests of algorithms for gait event estimation against a common reference, possible.

Innovative research on the construction of China’s youth physical health promotion service system based on sensor technology1

There is a certain correlation between human health and actual exercise. This has always been one of the focuses of many scholars. When analyzing the movement of human body, a lot of modern technology and equipment need to be used. In order to better realize the physical health of young people in our country, the health service system based on sensor technology has been put forward. By introducing accelerometer technology, real-time data capture and analysis of human motion posture and movement trajectory were carried out. At the same time, the reliability of the algorithm was improved. In order to verify the feasibility of the method, a practical case was verified. Experimental results showed that the method was more scientific and reasonable in motion capture analysis.

K, R. VK. A wireless IOT system towards gait detection technique using FSR sensor and wearable IOT devices

Purpose

The recent advancement in gait analysis combines internet of things that provides better observations of person living behavior. The biomechanical model used for elderly and physically challenged persons is related to gait-related parameters, and the accuracy of the existing systems significantly varies according to different person abilities and their challenges. The paper aims to discuss these issues.

Design/methodology/approach

Deployment of wearable sensors in gait analysis provides a better solution while tracking the changes of the personal style, and this proposed model uses an electronics system using force sensing resistor and body sensors.

Findings

Experimental results provide an average gait recognition of 95 percent compared to the existing neural network-based gait analysis model based on the walking speeds and threshold values.

Originality/value

The sensors are used to monitor and update the predicted values of a person for analysis. Using IoT a communication process is performed in the research work by identifying a physically challenged person even in crowded areas.