3D Tracking via Shoe Sensing

A global bibliometric and visualized analysis of gait analysis and artificial intelligence research from 1992 to 2022

Gait is an important basic function of human beings and an integral part of life. Many mental and physical abnormalities can cause noticeable differences in a person's gait. Abnormal gait can lead to serious consequences such as falls, limited mobility and reduced life satisfaction. Gait analysis, which includes joint kinematics, kinetics, and dynamic Electromyography (EMG) data, is now recognized as a clinically useful tool that can provide both quantifiable and qualitative information on performance to aid in treatment planning and evaluate its outcome. With the assistance of new artificial intelligence (AI) technology, the traditional medical environment has undergone great changes. AI has the potential to reshape medicine, making gait analysis more accurate, efficient and accessible. In this study, we analyzed basic information about gait analysis and AI articles that met inclusion criteria in the WoS Core Collection database from 1992-2022, and the VosViewer software was used for web visualization and keyword analysis. Through bibliometric and visual analysis, this article systematically introduces the research status of gait analysis and AI. We introduce the application of artificial intelligence in clinical gait analysis, which affects the identification and management of gait abnormalities found in various diseases. Machine learning (ML) and artificial neural networks (ANNs) are the most often utilized AI methods in gait analysis. By comparing the predictive capability of different AI algorithms in published studies, we evaluate their potential for gait analysis in different situations. Furthermore, the current challenges and future directions of gait analysis and AI research are discussed, which will also provide valuable reference information for investors in this field.

Wearable Sensor-Based Real-Time Gait Detection: A Systematic Review

Gait analysis has traditionally been carried out in a laboratory environment using expensive equipment, but, recently, reliable, affordable, and wearable sensors have enabled integration into clinical applications as well as use during activities of daily living. Real-time gait analysis is key to the development of gait rehabilitation techniques and assistive devices such as neuroprostheses. This article presents a systematic review of wearable sensors and techniques used in real-time gait analysis, and their application to pathological gait. From four major scientific databases, we identified 1262 articles of which 113 were analyzed in full-text. We found that heel strike and toe off are the most sought-after gait events. Inertial measurement units (IMU) are the most widely used wearable sensors and the shank and foot are the preferred placements. Insole pressure sensors are the most common sensors for ground-truth validation for IMU-based gait detection. Rule-based techniques relying on threshold or peak detection are the most widely used gait detection method. The heterogeneity of evaluation criteria prevented quantitative performance comparison of all methods. Although most studies predicted that the proposed methods would work on pathological gait, less than one third were validated on such data. Clinical applications of gait detection algorithms were considered, and we recommend a combination of IMU and rule-based methods as an optimal solution.

A Methodology for the Design of Application-Specific Cyber-Physical Social Sensing Co-Simulators

Cyber-Physical Social Sensing (CPSS) is a new trend in the context of pervasive sensing. In these new systems, various domains coexist in time, evolve together and influence each other. Thus, application-specific tools are necessary for specifying and validating designs and simulating systems. However, nowadays, different tools are employed to simulate each domain independently. Mainly, the cause of the lack of co-simulation instruments to simulate all domains together is the extreme difficulty of combining and synchronizing various tools. In order to reduce that difficulty, an adequate architecture for the final co-simulator must be selected. Therefore, in this paper the authors investigate and propose a methodology for the design of CPSS co-simulation tools. The paper describes the four steps that software architects should follow in order to design the most adequate co-simulator for a certain application, considering the final users’ needs and requirements and various additional factors such as the development team’s experience. Moreover, the first practical use case of the proposed methodology is provided. An experimental validation is also included in order to evaluate the performing of the proposed co-simulator and to determine the correctness of the proposal.