Literature DB >> 35632128

Wearables for Movement Analysis in Healthcare.

Paolo Capodaglio1,2, Veronica Cimolin3.   

Abstract

Quantitative movement analysis is widely used in clinical practice and research to objectively and thoroughly investigate movement disorder [...].

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Year:  2022        PMID: 35632128      PMCID: PMC9145753          DOI: 10.3390/s22103720

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.847


Quantitative movement analysis is widely used in clinical practice and research to objectively and thoroughly investigate movement disorder. Conventionally, body segment kinematic and kinetic parameters are measured in gait laboratories, using marker-based optoelectronic systems, force plates, and electromyographic systems. Although movement analysis is considered accurate, the availability of specific laboratories, high costs, and dependency on trained users sometimes limit its use in clinical practice. A variety of available compact wearable sensors have allowed researchers and clinicians to pursue applications in which individuals are monitored in the home and community settings, in different fields, such as movement analysis. Wearable sensors may contribute to the out-patient implementation of quantitative movement analysis for clinical purposes, thereby reducing evaluation times and unobtrusively and continuously providing objective and quantifiable data on the patients’ capabilities. We invited authors to submit their latest results in the field, either research articles or reviews articles, aimed at promoting novel wearable technology for movement analysis, methods for sensor signal processing, as well as on field experiences of their applications in healthcare. In total, 15 papers were accepted for publication in this Special Issue of Sensors, entitled “Wearables for Movement Analysis in Healthcare”. They are summarized in the subsequent paragraphs. The papers could be divided into three main categories: methodological applications, clinical applications, and sport applications. In terms of the methodological category, Zago et al. [1] estimated the gait parameters based on inertial sensors with machine learning techniques in healthy participants. Lueken et al. [2] presented a recently developed platform for a wireless body sensor network with customizable applications with a sensor setup for gait analysis during everyday life monitoring. Amitrano et al. [3] described a new wearable e-textile based system, named SWEET Sock, for the remote monitoring of biomedical signals and validated it by evaluating the agreement with an optoelectronic system for gait analysis on a set of free walk acquisitions. In clinical applications, wearable sensors were used in several pathological states, such as stroke [4,5,6], obese [7,8], and elderly [9] patients; patients with lower limb amputation [10]; patients with Parkinson’s disease [4,11,12]; and patients hospitalized for knee joint rehabilitation [13]. In particular, wearable systems were used both to quantify the functional limitations of the patients, during several movements (gait [5,7,8,9], upper limb [6,11], time up and go test [10], and unconstraint activities at home [12]) and to evaluate their accuracy and precision in comparison with the gold standard [4]. These papers support the clinical usability of wearable technology for clinical movement assessment. The applications in sport are more limited and they are focused on running and drop jump, forward sprint, and change in direction. Kim et al. [14] validated inertial measurement units (IMUs) for measuring ankle joint with a motion capture system during running in healthy individuals. Di Paolo et al. [15] quantified joint kinematics through a wearable sensor system in multidirectional high-speed complex movements after anterior cruciate ligament (ACL) injury, and validated it against a gold standard optoelectronic marker-based system. They demonstrated the use of wearable sensors as an alternative tool for motion capture system for assessing the performance and rehabilitation of athletes.
  15 in total

1.  Functional Electrical Stimulation for Foot Drop in Post-Stroke People: Quantitative Effects on Step-to-Step Symmetry of Gait Using a Wearable Inertial Sensor.

Authors:  Giulia Schifino; Veronica Cimolin; Massimiliano Pau; Maira Jaqueline da Cunha; Bruno Leban; Micaela Porta; Manuela Galli; Aline Souza Pagnussat
Journal:  Sensors (Basel)       Date:  2021-01-29       Impact factor: 3.576

2.  Evaluation and Application of a Customizable Wireless Platform: A Body Sensor Network for Unobtrusive Gait Analysis in Everyday Life.

Authors:  Markus Lueken; Leo Mueller; Michel G Decker; Cornelius Bollheimer; Steffen Leonhardt; Chuong Ngo
Journal:  Sensors (Basel)       Date:  2020-12-20       Impact factor: 3.576

3.  Rehabilitation and Return to Sport Assessment after Anterior Cruciate Ligament Injury: Quantifying Joint Kinematics during Complex High-Speed Tasks through Wearable Sensors.

Authors:  Stefano Di Paolo; Nicola Francesco Lopomo; Francesco Della Villa; Gabriele Paolini; Giulio Figari; Laura Bragonzoni; Alberto Grassi; Stefano Zaffagnini
Journal:  Sensors (Basel)       Date:  2021-03-26       Impact factor: 3.576

Review 4.  A Systematic Review of Diagnostic Accuracy and Clinical Applications of Wearable Movement Sensors for Knee Joint Rehabilitation.

Authors:  Robert Prill; Marina Walter; Aleksandra Królikowska; Roland Becker
Journal:  Sensors (Basel)       Date:  2021-12-09       Impact factor: 3.576

5.  A Comparative Analysis of Shoes Designed for Subjects with Obesity Using a Single Inertial Sensor: Preliminary Results.

Authors:  Veronica Cimolin; Michele Gobbi; Camillo Buratto; Samuele Ferraro; Andrea Fumagalli; Manuela Galli; Paolo Capodaglio
Journal:  Sensors (Basel)       Date:  2022-01-20       Impact factor: 3.576

6.  Computation of Gait Parameters in Post Stroke and Parkinson's Disease: A Comparative Study Using RGB-D Sensors and Optoelectronic Systems.

Authors:  Veronica Cimolin; Luca Vismara; Claudia Ferraris; Gianluca Amprimo; Giuseppe Pettiti; Roberto Lopez; Manuela Galli; Riccardo Cremascoli; Serena Sinagra; Alessandro Mauro; Lorenzo Priano
Journal:  Sensors (Basel)       Date:  2022-01-21       Impact factor: 3.576

7.  Rapid Dynamic Naturalistic Monitoring of Bradykinesia in Parkinson's Disease Using a Wrist-Worn Accelerometer.

Authors:  Jeroen G V Habets; Christian Herff; Pieter L Kubben; Mark L Kuijf; Yasin Temel; Luc J W Evers; Bastiaan R Bloem; Philip A Starr; Ro'ee Gilron; Simon Little
Journal:  Sensors (Basel)       Date:  2021-11-26       Impact factor: 3.576

8.  Sensor Network for Analyzing Upper Body Strategies in Parkinson's Disease versus Normative Kinematic Patterns.

Authors:  Paola Romano; Sanaz Pournajaf; Marco Ottaviani; Annalisa Gison; Francesco Infarinato; Claudia Mantoni; Maria Francesca De Pandis; Marco Franceschini; Michela Goffredo
Journal:  Sensors (Basel)       Date:  2021-05-31       Impact factor: 3.576

9.  Measurement of Ankle Joint Movements Using IMUs during Running.

Authors:  Byong Hun Kim; Sung Hyun Hong; In Wook Oh; Yang Woo Lee; In Ho Kee; Sae Yong Lee
Journal:  Sensors (Basel)       Date:  2021-06-21       Impact factor: 3.576

10.  Assessment of Upper Limb Movement Impairments after Stroke Using Wearable Inertial Sensing.

Authors:  Anne Schwarz; Miguel M C Bhagubai; Gerjan Wolterink; Jeremia P O Held; Andreas R Luft; Peter H Veltink
Journal:  Sensors (Basel)       Date:  2020-08-24       Impact factor: 3.576

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