An evaluation of inertial sensor technology in the discrimination of human gait

Many motion sensor-based applications have been developed in recent years because they provide useful information about daily activities and current health status of users. However, most of these applications require knowledge of sensor positions. Therefore, this research focused on the problem of detecting sensor positions. We collected standing-still and walking sensor data at various body positions from ten subjects. The offset values were removed by subtracting the sensor data of standing-still phase from the walking data for each axis of each sensor unit. Our hierarchical classification technique is based on optimizing local classifiers. Many common features are computed, and informative features are selected for specific classifications. In this approach, local classifiers such as arm-side and hand-side discriminations yielded F1-scores of 0.99 and 1.00, correspondingly. Overall, the proposed method achieved an F1-score of 0.81 and 0.84 using accelerometers and gyroscopes, respectively. Furthermore, we also discuss contributive features and parameter tuning in this analysis.

Section: Methodsmentioning

confidence: 99%

On-Body Sensor Positions Hierarchical Classification

Sang

Yano

Kondo

2018

“…For swimming, sensor placement at the trunk/limbs/head was used to provide either arm strokes (for front crawl [ 63 , 80 , 107 , 110 , 263 ]), kick strokes (for front crawl [ 124 , 126 ] and freestyle [ 125 ]), or generic strokes (for front crawl [ 24 , 68 , 85 , 108 , 156 , 164 , 168 , 263 , 323 , 332 ], butterfly [ 24 , 68 , 85 , 156 ], breaststroke [ 24 , 68 , 85 , 164 , 291 ]). Stride and step frequency/duration have been assessed during running [ 153 , 157 , 160 , 162 , 174 , 191 , 202 , 204 , 229 , 230 , 242 , 243 , 262 , 294 , 295 , 302 , 321 , 327 ], skating [ 293 ], and in the run up of cricket ball delivery [ 264 ]. Revolution rate was characterised for ...…”

Section: Trendsmentioning

confidence: 99%

Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review

Camomilla

Bergamini

Fantozzi

et al. 2018

376

302

Recent technological developments have led to the production of inexpensive, non-invasive, miniature magneto-inertial sensors, ideal for obtaining sport performance measures during training or competition. This systematic review evaluates current evidence and the future potential of their use in sport performance evaluation. Articles published in English (April 2017) were searched in Web-of-Science, Scopus, Pubmed, and Sport-Discus databases. A keyword search of titles, abstracts and keywords which included studies using accelerometers, gyroscopes and/or magnetometers to analyse sport motor-tasks performed by athletes (excluding risk of injury, physical activity, and energy expenditure) resulted in 2040 papers. Papers and reference list screening led to the selection of 286 studies and 23 reviews. Information on sport, motor-tasks, participants, device characteristics, sensor position and fixing, experimental setting and performance indicators was extracted. The selected papers dealt with motor capacity assessment (51 papers), technique analysis (163), activity classification (19), and physical demands assessment (61). Focus was placed mainly on elite and sub-elite athletes (59%) performing their sport in-field during training (62%) and competition (7%). Measuring movement outdoors created opportunities in winter sports (8%), water sports (16%), team sports (25%), and other outdoor activities (27%). Indications on the reliability of sensor-based performance indicators are provided, together with critical considerations and future trends.

“…Therefore, it is not strictly defined as "the duration of loss ground contact". According to [9,14], for the i-th step, the LOGC T expression is equal to…”

Section: Logc Timingmentioning

confidence: 99%

IART: Inertial Assistant Referee and Trainer for Race Walking

Caporaso

Grazioso

2020

This paper presents IART, a novel inertial wearable system for automatic detection of infringements and analysis of sports performance in race walking. IART algorithms are developed from raw inertial measurements collected by a single sensor located at the bottom of the vertebral column (L5–S1). Two novel parameters are developed to estimate infringements: loss of ground contact time and loss of ground contact step classification; three classic parameters are indeed used to estimate performance: step length ratio, step cadence, and smoothness. From these parameters, five biomechanical indices customized for elite athletes are derived. The experimental protocol consists of four repetitions of a straight path of 300 m on a long-paved road, performed by nine elite athletes. Over a total of 1620 steps (54 sequences of 30 steps each), the average accuracy of correct detection of loss of ground contact events is equal to 99%, whereas the correct classification of the infringement is equal to 87% for each step sequence, with a 92% of acceptable classifications. A great emphasis is dedicated on the user-centered development of IART: an intuitive radar chart representation is indeed developed to provide practical usability and interpretation of IART indices from the athletes, coaches, and referees perspectives. The results of IART, in terms of accuracy of its indices and usability from end-users, are encouraging for its usage as tool to support athletes and coaches in training and referees in real competitions.