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Sensors for Gait, Posture, Cognition and Health Monitoring

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

Deadline for manuscript submissions: closed (15 August 2020) | Viewed by 4769

Special Issue Editor


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Guest Editor
REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, University of Hasselt, 3590 Diepenbeek, Belgium
Interests: technology; assessment; big data; rehabilitation; epidemiology; sports science; exercise performance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Over the last several years, there have been major advances in the development and use of new technologies. The most important point is probably not the exponential curve of technological development, but the speed at which this technology is becoming widely available to the public. Smartphones, connected watches, motion tracking systems, and other smart clothes have turned people into connected bodies—true sensors in their own right. While these technologies are widely used by the public, they are only used in limited cases in clinics and even less during clinical trials. There is currently a significant shift in the way the patients are being assessed: from the strict and validated clinical environment (e.g., motion analysis laboratory) to real-life measurements.

This new paradigm offers new exciting perspectives both in clinics (allowing longitudinal follow-up, while currently only transverse assessments are mostly performed with only a few evaluations, ecological evaluation, cost-effective) and in research (big data, highly multidimensional analysis, etc.).

However, before being used in daily clinics, these new technologies must be validated. Besides the validation, many other questions must also be answered, such as the ethical issues related to the recording, storage, and analysis of the data using mobile sensors, the assessment of the quality of the data, the integration of those new data in the traditional clinical pipeline, the development of machine learning methods for heterogeneous big data, the cost and the integration of those evaluations in healthcare services, etc.

In this Special Issue, you are invited to submit contributions describing the development of methods, hardware or software, for optimized sensors design for functional evaluation (including but not limited to gait, balance, cognitive assessment, physical activity level), together with their application in different fields related to functional assessment and medical diagnosis.

Dr. Bruno Bonnechère
Guest Editor

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Keywords

  • new technology
  • development
  • validation
  • functional assessment
  • real-world data
  • big data

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Published Papers (2 papers)

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Research

18 pages, 3259 KiB  
Article
How Many Days are Necessary to Represent Typical Daily Leg Movement Behavior for Infants at Risk of Developmental Disabilities?
by Weiyang Deng, Ryota Nishiyori, Douglas L. Vanderbilt and Beth A. Smith
Sensors 2020, 20(18), 5344; https://doi.org/10.3390/s20185344 - 18 Sep 2020
Cited by 4 | Viewed by 2008
Abstract
Background: Movement characteristics can differentiate between infants at risk and infants with typical development. However, it is unknown how many days are needed to accurately represent typical daily behavior for infants at risk of developmental disabilities when using wearable sensors. To consider the [...] Read more.
Background: Movement characteristics can differentiate between infants at risk and infants with typical development. However, it is unknown how many days are needed to accurately represent typical daily behavior for infants at risk of developmental disabilities when using wearable sensors. To consider the balance between participant burden and the amount of data collected and optimizing the efficiency of data collection, our study determined (1) how many days were necessary to represent typical movement behavior for infants at risk of developmental disabilities and (2) whether movement behavior was different on weekend days and weekdays. Methods: We used Opal wearable sensors to collect at least 5 days of 11 infants’ leg movement data. The standard (average of 5 days) was compared with four methods (average of the first 1/2/3/4 days) using the Bland–Altman plots and the Spearman correlation coefficient. We also compared the data from the average of 2 weekend days to the average of the first 2 weekdays for 8 infants. Results: The Spearman correlation coefficient comparing the average of the first 2 days of data and the standards were all above 0.7. The absolute differences between them were all below 10% of the standards. The Bland–Altman plots showed more than 90% of the data points comparing the average of 2 days and the standards fell into the limit of agreement for each variable. The absolute difference between weekend days and weekdays for the leg movement rate, duration, average acceleration, and peak acceleration was 15.2%, 1.7%, 6.8% and 6.3% of the corresponding standard, respectively. Conclusion: Our results suggest 2 days is the optimal amount of data to represent typical daily leg movement behavior of infants at risk of developmental disabilities while minimizing participant burden. Further, leg movement behavior did not differ distinctly across weekend days and weekdays. These results provide supportive evidence for an efficient amount of data collections when using wearable sensors to evaluate movement behavior in infants at risk of developmental disabilities. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, Cognition and Health Monitoring)
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28 pages, 2927 KiB  
Article
Assisted Living System with Adaptive Sensor’s Contribution
by Magdalena Smoleń and Piotr Augustyniak
Sensors 2020, 20(18), 5278; https://doi.org/10.3390/s20185278 - 15 Sep 2020
Cited by 1 | Viewed by 2257
Abstract
Multimodal sensing and data processing have become a common approach in modern assisted living systems. This is widely justified by the complementary properties of sensors based on different sensing paradigms. However, all previous proposals assume data fusion to be made based on fixed [...] Read more.
Multimodal sensing and data processing have become a common approach in modern assisted living systems. This is widely justified by the complementary properties of sensors based on different sensing paradigms. However, all previous proposals assume data fusion to be made based on fixed criteria. We proved that particular sensors show different performance depending on the subject’s activity and consequently present the concept of an adaptive sensor’s contribution. In the proposed prototype architecture, the sensor information is first unified and then modulated to prefer the most reliable sensors. We also take into consideration the dynamics of the subject’s behavior and propose two algorithms for the adaptation of sensors’ contribution, and discuss their advantages and limitations based on case studies. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, Cognition and Health Monitoring)
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