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Sensors for Assessing and Rehabilitating Posture, Balance and Gait in Children and Older Adults

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

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 2068

Special Issue Editor


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Guest Editor
Department of Physical Therapy, Federal Univesity of Pernambuco, Recife CEP 55018-070, Pernambuco, Brazil
Interests: physical therapy; balance; gait; motor skills; motor performance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The assessment of posture, balance, gait, and gross and fine motor skills has been increasingly necessary to devise prevention and rehabilitation strategies, whether in children or in older adults. In children, these assessments are able to identify lower performance and delays in motor development and motor performance, enabling timely rehabilitation to improve posture, balance, gait and other motor skills, such as manual activities, running and vertical jumping even in childhood, without major repercussions for the child in adult life. As for older adults, evaluations of these outcomes are important, as they are capable of predicting declines in physical and functional capacity, demonstrating how this population is at risk in relation to their independence, and functionality, above all, in relation to the risk of falls, enabling the early detection of these problems and implementing rehabilitation in a timely manner for older adults. Given the above, assessments with more sensitive instruments for detecting minor changes in these outcomes, such as sensors, may be more effective, useful and capable of identifying small changes early. Thus, this Special Issue intends to provide evidence of studies that used instruments with sensors to assess and rehabilitate human posture, balance, gait and gross and fine motor skills in children or in older adults, through instruments such as force platforms, computerized dynamic posturography, accelerometers, wearable sensors, inertial sensors, virtual environments, augmented reality and virtual reality, among other sensors.

Dr. Renato de Souza Melo
Guest Editor

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Keywords

  • balance
  • children
  • cochlear implant
  • deafness
  • dizziness
  • falls
  • gait
  • motor skills
  • older adults
  • physical therapy
  • perception
  • posture
  • sensors
  • walking
  • wearable sensors
  • vertigo
  • vestibular diseases
  • virtual reality

Published Papers (1 paper)

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Research

19 pages, 1216 KiB  
Article
Classifying Tremor Dominant and Postural Instability and Gait Difficulty Subtypes of Parkinson’s Disease from Full-Body Kinematics
by N. Jabin Gong, Gari D. Clifford, Christine D. Esper, Stewart A. Factor, J. Lucas McKay and Hyeokhyen Kwon
Sensors 2023, 23(19), 8330; https://doi.org/10.3390/s23198330 - 9 Oct 2023
Cited by 3 | Viewed by 1781
Abstract
Characterizing motor subtypes of Parkinson’s disease (PD) is an important aspect of clinical care that is useful for prognosis and medical management. Although all PD cases involve the loss of dopaminergic neurons in the brain, individual cases may present with different combinations of [...] Read more.
Characterizing motor subtypes of Parkinson’s disease (PD) is an important aspect of clinical care that is useful for prognosis and medical management. Although all PD cases involve the loss of dopaminergic neurons in the brain, individual cases may present with different combinations of motor signs, which may indicate differences in underlying pathology and potential response to treatment. However, the conventional method for distinguishing PD motor subtypes involves resource-intensive physical examination by a movement disorders specialist. Moreover, the standardized rating scales for PD rely on subjective observation, which requires specialized training and unavoidable inter-rater variability. In this work, we propose a system that uses machine learning models to automatically and objectively identify some PD motor subtypes, specifically Tremor-Dominant (TD) and Postural Instability and Gait Difficulty (PIGD), from 3D kinematic data recorded during walking tasks for patients with PD (MDS-UPDRS-III Score, 34.7 ± 10.5, average disease duration 7.5 ± 4.5 years). This study demonstrates a machine learning model utilizing kinematic data that identifies PD motor subtypes with a 79.6% F1 score (N = 55 patients with parkinsonism). This significantly outperformed a comparison model using classification based on gait features (19.8% F1 score). Variants of our model trained to individual patients achieved a 95.4% F1 score. This analysis revealed that both temporal, spectral, and statistical features from lower body movements are helpful in distinguishing motor subtypes. Automatically assessing PD motor subtypes simply from walking may reduce the time and resources required from specialists, thereby improving patient care for PD treatments. Furthermore, this system can provide objective assessments to track the changes in PD motor subtypes over time to implement and modify appropriate treatment plans for individual patients as needed. Full article
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