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Wearable Sensors for Human Activity Recognition, Motion Analysis, and mHealth
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Wearable Sensors for Human Activity Recognition, Motion Analysis, and mHealth

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 9169

Special Issue Editors

Prof. Dr. Christian Haubelt

E-Mail Website
Guest Editor
Institute of Applied Microelectronics and Computer Engineering, University of Rostock, 18051 Rostock, Germany
Interests: embedded and cyber-physical systems; system-level design methodologies; heterogeneous many-core architectures; systemc-based modeling and verification of embedded systems; design space exploration and multi-objective optimization
Dr. Florian Grützmacher

E-Mail Website
Guest Editor
Institute of Applied Microelectronics and Computer Engineering, University of Rostock, 18051 Rostock, Germany
Interests: model-based embedded system design and analysis; energy-efficient sensor-based activity recognition systems; embedded multiprocessor; cyber-physical; wearable sensor systems

Special Issue Information

Dear Colleagues,

In the past decades, wearable sensors have found their way into human activity recognition (HAR), motion analysis, and mobile health applications (mHealth) as small and unobtrusive devices. The advances in micro-electro-mechanical systems (MEMS) technologies have led to multi-modal but energy-efficient sensing capabilities. Domain-specific processing architectures integrated into sensor devices as Systems-in-Package (SiP) allow for sensor fusion, signal pre-processing, and the recognition of basic activities, gestures, or steps.

However, although MEMS-based sensors allow for energy-efficient sensing and pre-processing, their integration into wearable sensor devices with wireless communication abilities, complementary processing architectures, and additional environmental sensors demands for application-specific solutions in the context of human activity and context recognition, motion analysis, and mobile health systems, focusing on energy-efficiency, latency, and data throughput. These solutions regard the entire range from efficient software over communication and processing technologies to system design methodologies.

This Special Issue gathers novel contributions to application-specific energy-efficient, low latency, and high-throughput algorithms, software, hardware, communication, and system design methods of wearable sensors in the context of human activity recognition, motion analysis, and mobile health systems. Topics of interest include, but are not limited to:

- sensor signal pre-processing

- sensor signal fusion

- data reduction/signal compression

- hardware/software sensor synchronization

- approximate computing

- efficient data transmission

- body-area sensor network technologies

- application-specific processing architectures

- AI accelerators

- on-sensor feature extraction and machine learning

- application specific hardware/software co-design

- system design and analysis methodologies

Prof. Dr. Christian Haubelt
Dr. Florian Grützmacher
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (3 papers)

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12 pages, 543 KiB  
Article
Toward Mental Effort Measurement Using Electrodermal Activity Features
by William Romine, Noah Schroeder, Tanvi Banerjee and Josephine Graft
Sensors 2022, 22(19), 7363; https://doi.org/10.3390/s22197363 - 28 Sep 2022
Cited by 15 | Viewed by 2761
Abstract
The ability to monitor mental effort during a task using a wearable sensor may improve productivity for both work and study. The use of the electrodermal activity (EDA) signal for tracking mental effort is an emerging area of research. Through analysis of over [...] Read more.
The ability to monitor mental effort during a task using a wearable sensor may improve productivity for both work and study. The use of the electrodermal activity (EDA) signal for tracking mental effort is an emerging area of research. Through analysis of over 92 h of data collected with the Empatica E4 on a single participant across 91 different activities, we report on the efficacy of using EDA features getting at signal intensity, signal dispersion, and peak intensity for prediction of the participant’s self-reported mental effort. We implemented the logistic regression algorithm as an interpretable machine learning approach and found that features related to signal intensity and peak intensity were most useful for the prediction of whether the participant was in a self-reported high mental effort state; increased signal and peak intensity were indicative of high mental effort. When cross-validated by activity moderate predictive efficacy was achieved (AUC = 0.63, F1 = 0.63, precision = 0.64, recall = 0.63) which was significantly stronger than using the model bias alone. Predicting mental effort using physiological data is a complex problem, and our findings add to research from other contexts showing that EDA may be a promising physiological indicator to use for sensor-based self-monitoring of mental effort throughout the day. Integration of other physiological features related to heart rate, respiration, and circulation may be necessary to obtain more accurate predictions. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Activity Recognition, Motion Analysis, and mHealth)
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32 pages, 1402 KiB  
Article
What Actually Works for Activity Recognition in Scenarios with Significant Domain Shift: Lessons Learned from the 2019 and 2020 Sussex-Huawei Challenges
by Stefan Kalabakov, Simon Stankoski, Ivana Kiprijanovska, Andrejaana Andova, Nina Reščič, Vito Janko, Martin Gjoreski, Matjaž Gams and Mitja Luštrek
Sensors 2022, 22(10), 3613; https://doi.org/10.3390/s22103613 - 10 May 2022
Cited by 3 | Viewed by 2192
Abstract
From 2018 to 2021, the Sussex-Huawei Locomotion-Transportation Recognition Challenge presented different scenarios in which participants were tasked with recognizing eight different modes of locomotion and transportation using sensor data from smartphones. In 2019, the main challenge was using sensor data from one location [...] Read more.
From 2018 to 2021, the Sussex-Huawei Locomotion-Transportation Recognition Challenge presented different scenarios in which participants were tasked with recognizing eight different modes of locomotion and transportation using sensor data from smartphones. In 2019, the main challenge was using sensor data from one location to recognize activities with sensors in another location, while in the following year, the main challenge was using the sensor data of one person to recognize the activities of other persons. We use these two challenge scenarios as a framework in which to analyze the effectiveness of different components of a machine-learning pipeline for activity recognition. We show that: (i) selecting an appropriate (location-specific) portion of the available data for training can improve the F1 score by up to 10 percentage points (p. p.) compared to a more naive approach, (ii) separate models for human locomotion and for transportation in vehicles can yield an increase of roughly 1 p. p., (iii) using semi-supervised learning can, again, yield an increase of roughly 1 p. p., and (iv) temporal smoothing of predictions with Hidden Markov models, when applicable, can bring an improvement of almost 10 p. p. Our experiments also indicate that the usefulness of advanced feature selection techniques and clustering to create person-specific models is inconclusive and should be explored separately in each use-case. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Activity Recognition, Motion Analysis, and mHealth)
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19 pages, 2149 KiB  
Technical Note
Modulation Spectral Signal Representation for Quality Measurement and Enhancement of Wearable Device Data: A Technical Note
by Abhishek Tiwari, Raymundo Cassani, Shruti Kshirsagar, Diana P. Tobon, Yi Zhu and Tiago H. Falk
Sensors 2022, 22(12), 4579; https://doi.org/10.3390/s22124579 - 17 Jun 2022
Cited by 6 | Viewed by 3471
Abstract
Wearable devices are burgeoning, and applications across numerous verticals are emerging, including human performance monitoring, at-home patient monitoring, and health tracking, to name a few. Off-the-shelf wearables have been developed with focus on portability, usability, and low-cost. As such, when deployed in highly [...] Read more.
Wearable devices are burgeoning, and applications across numerous verticals are emerging, including human performance monitoring, at-home patient monitoring, and health tracking, to name a few. Off-the-shelf wearables have been developed with focus on portability, usability, and low-cost. As such, when deployed in highly ecological settings, wearable data can be corrupted by artifacts and by missing data, thus severely hampering performance. In this technical note, we overview a signal processing representation called the modulation spectrum. The representation quantifies the rate-of-change of different spectral magnitude components and is shown to separate signal from noise, thus allowing for improved quality measurement, quality enhancement, and noise-robust feature extraction, as well as for disease characterization. We provide an overview of numerous applications developed by the authors over the last decade spanning different wearable modalities and list the results obtained from experimental results alongside comparisons with various state-of-the-art benchmark methods. Open-source software is showcased with the hope that new applications can be developed. We conclude with a discussion on possible future research directions, such as context awareness, signal compression, and improved input representations for deep learning algorithms. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Activity Recognition, Motion Analysis, and mHealth)
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