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Accelerometer and Its Application in Health Research
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Accelerometer and Its Application in Health Research

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

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 24442

Special Issue Editor

Dr. Daniel Arvidsson

E-Mail Website
Guest Editor
Center for Health and Performance, Department of Food and Nutrition and Sport Science, University of Gothenburg, Gothenburg, Sweden
Interests: physical activity methodology; statistical methods for pattern analysis; relationships with health variables

Special Issue Information

Dear Colleagues,

Accelerometers are widely used in clinical and epidemiological research to provide objective measures of physical activity. As physical inactivity becomes an increasingly serious health concern for society, there is a great interest in using accelerometers for characterizing physical activity in individuals. The importance of physical activity and its unequal distribution in society has become more apparent in light of the COVID-19 pandemic.

Researchers face several challenges when assessing individuals’ physical activity behaviours. These challenges concern placing sensors on the body, processing raw accelerometer data into to useful and comprehensive measures, and investigating the complete behavior and its importance to health.

This Special Issue of Sensors highlights advances in the use of accelerometer data and statistical methods to investigate physical activity and its relation to health. Topics include the following:

Accelerometers (stability and calibration)
Algorithm development (linear versus non-linear data structures)
Measurement errors
Physical activity pattern analysis
Physical activity patterns and health

Assoc. Prof. Daniel Arvidsson
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Keywords

  • accelerometers
  • data processing
  • calibration
  • reliability
  • algorithms
  • validity
  • physical activity intensity
  • activity type
  • body position
  • sedentary behavior
  • pattern analysis
  • multivariate data analysis
  • compositional data analysis
  • collinearity

Published Papers (6 papers)

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Research

16 pages, 2275 KiB  
Article
A Novel Mobile Device-Based Approach to Quantitative Mobility Measurements for Power Wheelchair Users
by Jicheng Fu, Shuai Zhang, Hongwu Wang, Yan Daniel Zhao and Gang Qian
Sensors 2021, 21(24), 8275; https://doi.org/10.3390/s21248275 - 10 Dec 2021
Viewed by 2180
Abstract
This study is motivated by the fact that there are currently no widely used applications available to quantitatively measure a power wheelchair user’s mobility, which is an important indicator of quality of life. To address this issue, we propose an approach that allows [...] Read more.
This study is motivated by the fact that there are currently no widely used applications available to quantitatively measure a power wheelchair user’s mobility, which is an important indicator of quality of life. To address this issue, we propose an approach that allows power wheelchair users to use their own mobile devices, e.g., a smartphone or smartwatch, to non-intrusively collect mobility data in their daily life. However, the convenience of data collection brings substantial challenges in data analysis because the data patterns associated with wheelchair maneuvers are not as strong as other activities, e.g., walking, running, etc. In addition, the built-in sensors in different mobile devices create significant heterogeneity in terms of sensitivity, noise patterns, sampling settings, etc. To address the aforementioned challenges, we developed a novel approach composed of algorithms that work collaboratively to reduce noise, identify patterns intrinsic to wheelchair maneuvers, and finalize mobility analysis by removing spikes and dips caused by abrupt maneuver changes. We conducted a series of experiments to evaluate the proposed approach. Experimental results showed that our approach could accurately determine wheelchair maneuvers regardless of the models and placements of the mobile devices. Full article
(This article belongs to the Special Issue Accelerometer and Its Application in Health Research)
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18 pages, 1644 KiB  
Article
Clustering Accelerometer Activity Patterns from the UK Biobank Cohort
by Stephen Clark, Nik Lomax, Michelle Morris, Francesca Pontin and Mark Birkin
Sensors 2021, 21(24), 8220; https://doi.org/10.3390/s21248220 - 09 Dec 2021
Cited by 6 | Viewed by 3332
Abstract
Many researchers are beginning to adopt the use of wrist-worn accelerometers to objectively measure personal activity levels. Data from these devices are often used to summarise such activity in terms of averages, variances, exceedances, and patterns within a profile. In this study, we [...] Read more.
Many researchers are beginning to adopt the use of wrist-worn accelerometers to objectively measure personal activity levels. Data from these devices are often used to summarise such activity in terms of averages, variances, exceedances, and patterns within a profile. In this study, we report the development of a clustering utilising the whole activity profile. This was achieved using the robust clustering technique of k-medoids applied to an extensive data set of over 90,000 activity profiles, collected as part of the UK Biobank study. We identified nine distinct activity profiles in these data, which captured both the pattern of activity throughout a week and the intensity of the activity: “Active 9 to 5”, “Active”, “Morning Movers”, “Get up and Active”, “Live for the Weekend”, “Moderates”, “Leisurely 9 to 5”, “Sedate” and “Inactive”. These patterns are differentiated by sociodemographic, socioeconomic, and health and circadian rhythm data collected by UK Biobank. The utility of these findings are that they sit alongside existing summary measures of physical activity to provide a way to typify distinct activity patterns that may help to explain other health and morbidity outcomes, e.g., BMI or COVID-19. This research will be returned to the UK Biobank for other researchers to use. Full article
(This article belongs to the Special Issue Accelerometer and Its Application in Health Research)
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13 pages, 4240 KiB  
Article
Gait Phase Estimation by Using LSTM in IMU-Based Gait Analysis—Proof of Concept
by Mustafa Sarshar, Sasanka Polturi and Lutz Schega
Sensors 2021, 21(17), 5749; https://doi.org/10.3390/s21175749 - 26 Aug 2021
Cited by 27 | Viewed by 6158
Abstract
Gait phase detection in IMU-based gait analysis has some limitations due to walking style variations and physical impairments of individuals. Therefore, available algorithms may not work properly when the gait data is noisy, or the person rarely reaches a steady state of walking. [...] Read more.
Gait phase detection in IMU-based gait analysis has some limitations due to walking style variations and physical impairments of individuals. Therefore, available algorithms may not work properly when the gait data is noisy, or the person rarely reaches a steady state of walking. The aim of this work was to employ Artificial Intelligence (AI), specifically a long short-term memory (LSTM) algorithm, to overcome these weaknesses. Three supervised LSTM-based models were designed to estimate the expected gait phases, including foot-off (FO), mid-swing (MidS) and foot-contact (FC). For collecting gait data two tri-axial inertial sensors were located above each ankle. The angular velocity magnitude, rotation matrix magnitude and free acceleration magnitude were captured for data labeling and turning detection and to strengthen the model, respectively. To do so, a train dataset based on a novel movement protocol was acquired. A validation dataset similar to a train dataset was generated as well. Five test datasets from already existing data were also created to independently evaluate the models. After testing the models on validation and test datasets, all three models demonstrated promising performance in estimating desired gait phases. The proposed approach proves the possibility of employing AI-based algorithms to predict labeled gait phases from a time series of gait data. Full article
(This article belongs to the Special Issue Accelerometer and Its Application in Health Research)
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16 pages, 11106 KiB  
Article
A Capacitive 3-Axis MEMS Accelerometer for Medipost: A Portable System Dedicated to Monitoring Imbalance Disorders
by Michał Szermer, Piotr Zając, Piotr Amrozik, Cezary Maj, Mariusz Jankowski, Grzegorz Jabłoński, Rafał Kiełbik, Jacek Nazdrowicz, Małgorzata Napieralska and Bartosz Sakowicz
Sensors 2021, 21(10), 3564; https://doi.org/10.3390/s21103564 - 20 May 2021
Cited by 12 | Viewed by 5787
Abstract
The constant development and miniaturization of MEMS sensors invariably provides new possibilities for their use in health-related and medical applications. The application of MEMS devices in posturographic systems allows faster diagnosis and significantly facilitates the work of medical staff. MEMS accelerometers constitute a [...] Read more.
The constant development and miniaturization of MEMS sensors invariably provides new possibilities for their use in health-related and medical applications. The application of MEMS devices in posturographic systems allows faster diagnosis and significantly facilitates the work of medical staff. MEMS accelerometers constitute a vital part of such systems, particularly those intended for monitoring patients with imbalance disorders. The correct design of such sensors is crucial for gathering data about patient movement and ensuring the good overall performance of the entire system. This paper presents the design and measurements of a three-axis accelerometer dedicated for use in a device which tracks patient movement. Its main focus is the characterization of the sensor, comparing different designs and evaluating the impact of the packaging and readout circuit integration on sensor operation. Extensive testing and measurements confirm that the designed accelerometer works correctly and allows identifying the best design in terms of sensitivity/stability. Moreover, the response of the proposed sensor as a function of the applied acceleration demonstrates very good linearity only if the readout circuit is integrated in the same package as the MEMS sensor. Full article
(This article belongs to the Special Issue Accelerometer and Its Application in Health Research)
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16 pages, 720 KiB  
Article
Observational and Accelerometer Analysis of Head Movement Patterns in Psychotherapeutic Dialogue
by Masashi Inoue, Toshio Irino, Nobuhiro Furuyama and Ryoko Hanada
Sensors 2021, 21(9), 3162; https://doi.org/10.3390/s21093162 - 02 May 2021
Cited by 4 | Viewed by 2403
Abstract
Psychotherapists, who use their communicative skills to assist people, review their dialogue practices and improve their skills from their experiences. However, technology has not been fully exploited for this purpose. In this study, we analyze the use of head movements during actual psychotherapeutic [...] Read more.
Psychotherapists, who use their communicative skills to assist people, review their dialogue practices and improve their skills from their experiences. However, technology has not been fully exploited for this purpose. In this study, we analyze the use of head movements during actual psychotherapeutic dialogues between two participants—therapist and client—using video recordings and head-mounted accelerometers. Accelerometers have been utilized in the mental health domain but not for analyzing mental health related communications. We examined the relationship between the state of the interaction and temporally varying head nod and movement patterns in psychological counseling sessions. Head nods were manually annotated and the head movements were measured using accelerometers. Head nod counts were analyzed based on annotations taken from video data. We conducted cross-correlation analysis of the head movements of the two participants using the accelerometer data. The results of two case studies suggest that upward and downward head nod count patterns may reflect stage transitions in counseling dialogues and that peaks of head movement synchrony may be related to emphasis in the interaction. Full article
(This article belongs to the Special Issue Accelerometer and Its Application in Health Research)
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11 pages, 1984 KiB  
Communication
Intensity Paradox—Low-Fit People Are Physically Most Active in Terms of Their Fitness
by Henri Vähä-Ypyä, Harri Sievänen, Pauliina Husu, Kari Tokola and Tommi Vasankari
Sensors 2021, 21(6), 2063; https://doi.org/10.3390/s21062063 - 15 Mar 2021
Cited by 21 | Viewed by 2853
Abstract
Depending on their cardiorespiratory fitness (CRF), people may perceive the exertion of incident physical activity (PA) differently. Therefore, the use of relative intensity thresholds based on individual fitness have been proposed to evaluate the accumulation of PA at different intensity levels. A subsample [...] Read more.
Depending on their cardiorespiratory fitness (CRF), people may perceive the exertion of incident physical activity (PA) differently. Therefore, the use of relative intensity thresholds based on individual fitness have been proposed to evaluate the accumulation of PA at different intensity levels. A subsample of the FinFit2017-study, 1952 adults (803 men and 1149 women) aged 20–69 years, participated in this study. Their maximal oxygen uptake (VO2max) was predicted with a 6 min walk test, and they were instructed to wear a triaxial hip-worn accelerometer for one week. The participants were divided into CRF tertiles by five age groups and sex. Raw acceleration data were analyzed with the mean amplitude deviation method in 6 s epochs. Additionally, the data were smoothed with 1 min and 6 min exponential moving averages. The absolute intensity threshold for moderate activity was 3.0 metabolic equivalent (MET) and for vigorous 6.0 MET. Correspondingly, the relative thresholds were 40% and 60% of the oxygen uptake reserve. Participants in the lowest CRF tertile were the most active with relative thresholds, and participants in the highest CRF tertile were the most active with absolute thresholds. High-fit people easily reached the absolute thresholds, while people in the lowest CRF tertile had to utilize most of their aerobic capacity on a daily basis simply to keep up with their daily chores or peers. Full article
(This article belongs to the Special Issue Accelerometer and Its Application in Health Research)
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