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Francisco L, Duarte J, Godinho AN, Zdravevski E, Albuquerque C, Pires IM, Coelho PJ. Sensor-based systems for the measurement of Functional Reach Test results: a systematic review. PeerJ Comput Sci 2024; 10:e1823. [PMID: 38660214 PMCID: PMC11042010 DOI: 10.7717/peerj-cs.1823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 12/26/2023] [Indexed: 04/26/2024]
Abstract
The measurement of Functional Reach Test (FRT) is a widely used assessment tool in various fields, including physical therapy, rehabilitation, and geriatrics. This test evaluates a person's balance, mobility, and functional ability to reach forward while maintaining stability. Recently, there has been a growing interest in utilizing sensor-based systems to objectively and accurately measure FRT results. This systematic review was performed in various scientific databases or publishers, including PubMed Central, IEEE Explore, Elsevier, Springer, the Multidisciplinary Digital Publishing Institute (MDPI), and the Association for Computing Machinery (ACM), and considered studies published between January 2017 and October 2022, related to methods for the automation of the measurement of the Functional Reach Test variables and results with sensors. Camera-based devices and motion-based sensors are used for Functional Reach Tests, with statistical models extracting meaningful information. Sensor-based systems offer several advantages over traditional manual measurement techniques, as they can provide objective and precise measurements of the reach distance, quantify postural sway, and capture additional parameters related to the movement.
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Affiliation(s)
- Luís Francisco
- School of Technology and Management, Polytechnic University of Leiria, Leiria, Portugal
| | - João Duarte
- School of Technology and Management, Polytechnic University of Leiria, Leiria, Portugal
| | | | - Eftim Zdravevski
- Faculty of Computer Science and Engineering, University of Sts. Cyril and Methodius, Skopje, North Macedonia
| | - Carlos Albuquerque
- Child Studies Research Center (CIEC), University of Minho, Braga, Portugal
- Higher School of Health, Polytechnic Institute of Viseu, Viseu, Portugal
- Nursing School of Coimbra (ESEnfC), Health Sciences Research Unit: Nursing (UICISA: E), Coimbra, Portugal
| | - Ivan Miguel Pires
- Instituto de Telecomunicações, Escola Superior de Tecnologia e Gestão de Águeda, Universidade de Aveiro, Águeda, Portugal
| | - Paulo Jorge Coelho
- School of Technology and Management, Polytechnic University of Leiria, Leiria, Portugal
- Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra), Coimbra, Portugal
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Francisco L, Duarte J, Albuquerque C, Albuquerque D, Pires IM, Coelho PJ. Mobile Data Gathering and Preliminary Analysis for the Functional Reach Test. SENSORS (BASEL, SWITZERLAND) 2024; 24:1301. [PMID: 38400459 PMCID: PMC10892343 DOI: 10.3390/s24041301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 02/11/2024] [Accepted: 02/15/2024] [Indexed: 02/25/2024]
Abstract
The functional reach test (FRT) is a clinical tool used to evaluate dynamic balance and fall risk in older adults and those with certain neurological diseases. It provides crucial information for developing rehabilitation programs to improve balance and reduce fall risk. This paper aims to describe a new tool to gather and analyze the data from inertial sensors to allow automation and increased reliability in the future by removing practitioner bias and facilitating the FRT procedure. A new tool for gathering and analyzing data from inertial sensors has been developed to remove practitioner bias and streamline the FRT procedure. The study involved 54 senior citizens using smartphones with sensors to execute FRT. The methods included using a mobile app to gather data, using sensor-fusion algorithms like the Madgwick algorithm to estimate orientation, and attempting to estimate location by twice integrating accelerometer data. However, accurate position estimation was difficult, highlighting the need for more research and development. The study highlights the benefits and drawbacks of automated balance assessment testing with mobile device sensors, highlighting the potential of technology to enhance conventional health evaluations.
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Affiliation(s)
- Luís Francisco
- Electrotechnical Department, Polytechnic University of Leiria, 2411-901 Leiria, Portugal
| | - João Duarte
- Electrotechnical Department, Polytechnic University of Leiria, 2411-901 Leiria, Portugal
| | - Carlos Albuquerque
- Health Sciences Research Unit: Nursing (UICISA: E), Nursing School of Coimbra (ESEnfC), 3004-011 Coimbra, Portugal;
- Higher School of Health, Polytechnic Institute of Viseu, 3504-510 Viseu, Portugal
- Child Studies Research Center (CIEC), University of Minho, 4710-057 Braga, Portugal
| | - Daniel Albuquerque
- Instituto de Telecomunicações, Escola Superior de Tecnologia e Gestão de Águeda, Universidade de Aveiro, 3750-127 Águeda, Portugal; (D.A.); (I.M.P.)
| | - Ivan Miguel Pires
- Instituto de Telecomunicações, Escola Superior de Tecnologia e Gestão de Águeda, Universidade de Aveiro, 3750-127 Águeda, Portugal; (D.A.); (I.M.P.)
| | - Paulo Jorge Coelho
- Electrotechnical Department, Polytechnic University of Leiria, 2411-901 Leiria, Portugal
- Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra), 3030-290 Coimbra, Portugal
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Ferraris C, Ronga I, Pratola R, Coppo G, Bosso T, Falco S, Amprimo G, Pettiti G, Lo Priore S, Priano L, Mauro A, Desideri D. Usability of the REHOME Solution for the Telerehabilitation in Neurological Diseases: Preliminary Results on Motor and Cognitive Platforms. SENSORS (BASEL, SWITZERLAND) 2022; 22:9467. [PMID: 36502170 PMCID: PMC9740672 DOI: 10.3390/s22239467] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 11/30/2022] [Accepted: 12/01/2022] [Indexed: 06/17/2023]
Abstract
The progressive aging of the population and the consequent growth of individuals with neurological diseases and related chronic disabilities, will lead to a general increase in the costs and resources needed to ensure treatment and care services. In this scenario, telemedicine and e-health solutions, including remote monitoring and rehabilitation, are attracting increasing interest as tools to ensure the sustainability of the healthcare system or, at least, to support the burden for health care facilities. Technological advances in recent decades have fostered the development of dedicated and innovative Information and Communication Technology (ICT) based solutions, with the aim of complementing traditional care and treatment services through telemedicine applications that support new patient and disease management strategies. This is the background for the REHOME project, whose technological solution, presented in this paper, integrates innovative methodologies and devices for remote monitoring and rehabilitation of cognitive, motor, and sleep disorders associated with neurological diseases. One of the primary goals of the project is to meet the needs of patients and clinicians, by ensuring continuity of treatment from healthcare facilities to the patient's home. To this end, it is important to ensure the usability of the solution by elderly and pathological individuals. Preliminary results of usability and user experience questionnaires on 70 subjects recruited in three experimental trials are presented here.
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Affiliation(s)
- Claudia Ferraris
- Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, 10129 Turin, Italy
| | - Irene Ronga
- BraIn Plasticity and Behaviour Changes Research Group, Department of Psychology, University of Turin, 10124 Turin, Italy
| | - Roberto Pratola
- Engineering Ingegneria Informatica S.p.A., 00144 Rome, Italy
| | - Guido Coppo
- Synarea Consultants s.r.l., 10153 Turin, Italy
| | - Tea Bosso
- BraIn Plasticity and Behaviour Changes Research Group, Department of Psychology, University of Turin, 10124 Turin, Italy
- Geriatrics Unit, Città della Salute e della Scienza Hospital, 10126 Turin, Italy
| | - Sara Falco
- BraIn Plasticity and Behaviour Changes Research Group, Department of Psychology, University of Turin, 10124 Turin, Italy
- Clinical Pyschology Unit, Città della Salute e della Scienza Hospital, 10126 Turin, Italy
| | - Gianluca Amprimo
- Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, 10129 Turin, Italy
- Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy
| | - Giuseppe Pettiti
- Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, 10129 Turin, Italy
| | | | - Lorenzo Priano
- Istituto Auxologico Italiano, IRCCS, Department of Neurology and Neurorehabilitation, S. Giuseppe Hospital, 20123 Milan, Italy
- Department of Neurosciences, University of Turin, 10126 Turin, Italy
| | - Alessandro Mauro
- Istituto Auxologico Italiano, IRCCS, Department of Neurology and Neurorehabilitation, S. Giuseppe Hospital, 20123 Milan, Italy
- Department of Neurosciences, University of Turin, 10126 Turin, Italy
| | - Debora Desideri
- Engineering Ingegneria Informatica S.p.A., 00144 Rome, Italy
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Amprimo G, Masi G, Priano L, Azzaro C, Galli F, Pettiti G, Mauro A, Ferraris C. Assessment Tasks and Virtual Exergames for Remote Monitoring of Parkinson's Disease: An Integrated Approach Based on Azure Kinect. SENSORS (BASEL, SWITZERLAND) 2022; 22:8173. [PMID: 36365870 PMCID: PMC9654712 DOI: 10.3390/s22218173] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 10/17/2022] [Accepted: 10/22/2022] [Indexed: 06/16/2023]
Abstract
Motor impairments are among the most relevant, evident, and disabling symptoms of Parkinson’s disease that adversely affect quality of life, resulting in limited autonomy, independence, and safety. Recent studies have demonstrated the benefits of physiotherapy and rehabilitation programs specifically targeted to the needs of Parkinsonian patients in supporting drug treatments and improving motor control and coordination. However, due to the expected increase in patients in the coming years, traditional rehabilitation pathways in healthcare facilities could become unsustainable. Consequently, new strategies are needed, in which technologies play a key role in enabling more frequent, comprehensive, and out-of-hospital follow-up. The paper proposes a vision-based solution using the new Azure Kinect DK sensor to implement an integrated approach for remote assessment, monitoring, and rehabilitation of Parkinsonian patients, exploiting non-invasive 3D tracking of body movements to objectively and automatically characterize both standard evaluative motor tasks and virtual exergames. An experimental test involving 20 parkinsonian subjects and 15 healthy controls was organized. Preliminary results show the system’s ability to quantify specific and statistically significant (p < 0.05) features of motor performance, easily monitor changes as the disease progresses over time, and at the same time permit the use of exergames in virtual reality both for training and as a support for motor condition assessment (for example, detecting an average reduction in arm swing asymmetry of about 14% after arm training). The main innovation relies precisely on the integration of evaluative and rehabilitative aspects, which could be used as a closed loop to design new protocols for remote management of patients tailored to their actual conditions.
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Affiliation(s)
- Gianluca Amprimo
- Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
- Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Giulia Masi
- Department of Neurosciences, University of Turin, Via Cherasco 15, 10100 Torino, Italy
| | - Lorenzo Priano
- Department of Neurosciences, University of Turin, Via Cherasco 15, 10100 Torino, Italy
- Istituto Auxologico Italiano, IRCCS, S. Giuseppe Hospital, Strada Luigi Cadorna 90, 28824 Piancavallo, Italy
| | - Corrado Azzaro
- Istituto Auxologico Italiano, IRCCS, S. Giuseppe Hospital, Strada Luigi Cadorna 90, 28824 Piancavallo, Italy
| | - Federica Galli
- Istituto Auxologico Italiano, IRCCS, S. Giuseppe Hospital, Strada Luigi Cadorna 90, 28824 Piancavallo, Italy
| | - Giuseppe Pettiti
- Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Alessandro Mauro
- Department of Neurosciences, University of Turin, Via Cherasco 15, 10100 Torino, Italy
- Istituto Auxologico Italiano, IRCCS, S. Giuseppe Hospital, Strada Luigi Cadorna 90, 28824 Piancavallo, Italy
| | - Claudia Ferraris
- Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
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