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Sensors for Non-intrusive Human Activity Monitoring

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Dr. Juan-Manuel Belda-Lois

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Instituto de Biomecánica de Valencia, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: biomechanics; physiological signals; functional assessment

Special Issue Information

Dear Colleagues,

There are an increasing number of sensors around us, allowing non-intrusive monitoring of relevant aspects of health, quality of life, and safety in the workplace. These sensors have the capacity to provide valuable information on the interaction of people with the environment and on the state of the person.

These sensors encompass biomechanical and physiological information that can allow us to estimate the effort, cognitive load, attentional state, and health status in real conditions.

Therefore, we aim to collect research works for the development of sensors to non-intrusively monitor human activities, as well as the algorithms for providing reliable and processing information, and for integrating data from different sources.

Topics of interest include but are not limited to the following:

Sensors for biomechanical assessment out of lab conditions;
Sensors for reliable analysis of cognitive and emotional state;
Sensors for monitoring human activities in different environments (workplace, healthcare facilities, home).

Dr. Juan-Manuel Belda-Lois
Guest Editor

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Research

18 pages, 1013 KiB

Open AccessArticle

Sound of Daily Living Identification Based on Hierarchical Situation Audition

by Jiaxuan Wu, Yunfei Feng and Carl K. Chang

Sensors 2023, 23(7), 3726; https://doi.org/10.3390/s23073726 - 4 Apr 2023

Viewed by 1708

Abstract

One of the key objectives in developing IoT applications is to automatically detect and identify human activities of daily living (ADLs). Mobile phone users are becoming more accepting of sharing data captured by various built-in sensors. Sounds detected by smartphones are processed in this work. We present a hierarchical identification system to recognize ADLs by detecting and identifying certain sounds taking place in a complex audio situation (AS). Three major categories of sound are discriminated in terms of signal duration. These are persistent background noise (PBN), non-impulsive long sounds (NILS), and impulsive sound (IS). We first analyze audio signals in a situation-aware manner and then map the sounds of daily living (SDLs) to ADLs. A new hierarchical audible event (AE) recognition approach is proposed that classifies atomic audible actions (AAs), then computes pre-classified portions of atomic AAs energy in one AE session, and finally marks the maximum-likelihood ADL label as the outcome. Our experiments demonstrate that the proposed hierarchical methodology is effective in recognizing SDLs and, thus, also in detecting ADLs with a remarkable performance for other known baseline systems. Full article

(This article belongs to the Special Issue Sensors for Non-intrusive Human Activity Monitoring)

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10 pages, 1017 KiB

Open AccessArticle

Phases of Match-Play in Professional Australian Football: Positional Demands and Match-Related Fatigue

by Michael J. Rennie, Stephen J. Kelly, Stephen Bush, Robert W. Spurrs, William B. Sheehan and Mark L. Watsford

Sensors 2022, 22(24), 9887; https://doi.org/10.3390/s22249887 - 15 Dec 2022

Cited by 2 | Viewed by 1878

Abstract

This study examined the influence of player position and match quarter on activity profiles during the phases of play in Australian Football. Global positioning satellite data was collected for one season from an Australian Football League team for nomadic, key position and ruck players (age: 24.8 ± 4.2 years, body mass: 88.3 ± 8.7 kg, height: 1.88 ± 0.8 m). Separate linear mixed models and effect sizes were used to analyse differences between positions and game quarter within each phase of play for values of distance, speed and metabolic power indices. There were clear differences between positions for low-speed running, high-speed running, total distance and average speed. Nomadic players generally recorded the highest match running outputs, followed by key position players and ruckmen. Within each position, offence and defence involved the highest intensities, followed by contested play and then stoppage periods. Across the four quarters, there were small to large reductions in average speed, high-speed running, high power and energy expenditure during offence, defence and contested play, but not during stoppages. Accordingly, conditioning staff should consider the intermittent intensities of the phases of match-play for each position to optimally prepare players for competition. Reductions in match intensities were evident during active periods of play providing implications for real-time monitoring to optimise the timing of rotations. Full article

(This article belongs to the Special Issue Sensors for Non-intrusive Human Activity Monitoring)

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