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Coley C, Kovelman S, Belschner J, Cleary K, Schladen M, Evans SH, Salvador T, Monfaredi R, Fooladi Talari H, Slagle J, Rana MS. PedBotHome: A Video Game-Based Robotic Ankle Device Created for Home Exercise in Children With Neurological Impairments. Pediatr Phys Ther 2022; 34:212-219. [PMID: 35385456 PMCID: PMC9009250 DOI: 10.1097/pep.0000000000000881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE This pilot study assesses the feasibility of using PedBotHome to promote adherence to a home exercise program, the ability of the device to withstand frequent use, and changes in participant ankle mobility.PedBotHome is a robotic ankle device with integrated video game software designed to improve ankle mobility in children with cerebral palsy. METHODS Eight participants enrolled in a 28-day trial of PedBotHome. Ankle strength, range of motion, and plantar flexor spasticity were measured pre- and posttrial. Performance was monitored remotely, and game settings were modified weekly by physical therapists. RESULTS Four participants met the study goal of 20 days of use. There were statistically significant improvements in ankle strength, spasticity, and range of motion. CONCLUSIONS PedBotHome is a feasible device to engage children with static neurological injuries in ankle home exercise. This pilot study expands the paradigm for future innovative home-based robotic rehabilitation.
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Affiliation(s)
- Catherine Coley
- Physical Therapy (Drs Coley, Kovelman, and Belschner), Children's National Hospital, Washington, District of Columbia; Sheikh Zayed Research Institute (Drs Cleary and Monfaredi and Messrs Salvador, Fooladi Talari, Slagle, and Rana), Children's National Hospital, Washington, District of Columbia; Georgetown University (Dr Schladen), Washington, District of Columbia; Physical Medicine and Rehabilitation (Dr Evans), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Center for Surgical Care (Mr Rana), Children's National Hospital, Washington, District of Columbia
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Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review. INFORMATICS 2021. [DOI: 10.3390/informatics8040073] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
This article presents a systematic mapping study dedicated to conduct a literature review on machine learning and IoT applied in the identification of diseases through heart sounds. This research was conducted between January 2010 and July 2021, considering IEEE Xplore, PubMed Central, ACM Digital Library, JMIR—Journal of Medical Internet Research, Springer Library, and Science Direct. The initial search resulted in 4372 papers, and after applying the inclusion and exclusion criteria, 58 papers were selected for full reading to answer the research questions. The main results are: of the 58 articles selected, 46 (79.31%) mention heart rate observation methods with wearable sensors and digital stethoscopes, and 34 (58.62%) mention care with machine learning algorithms. The analysis of the studies based on the bibliometric network generated by the VOSviewer showed in 13 studies (22.41%) a trend related to the use of intelligent services in the prediction of diagnoses related to cardiovascular disorders.
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Moustris G, Kardaris N, Tsiami A, Chalvatzaki G, Koutras P, Dometios A, Oikonomou P, Tzafestas C, Maragos P, Efthimiou E, Papageorgiou X, Fotinea SE, Koumpouros Y, Vacalopoulou A, Papageorgiou E, Karavasili A, Koureta F, Dimou D, Nikolakakis A, Karaiskos K, Mavridis P. The i-Walk Lightweight Assistive Rollator: First Evaluation Study. Front Robot AI 2021; 8:677542. [PMID: 34604315 PMCID: PMC8483242 DOI: 10.3389/frobt.2021.677542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Accepted: 08/10/2021] [Indexed: 11/16/2022] Open
Abstract
Robots can play a significant role as assistive devices for people with movement impairment and mild cognitive deficit. In this paper we present an overview of the lightweight i-Walk intelligent robotic rollator, which offers cognitive and mobility assistance to the elderly and to people with light to moderate mobility impairment. The utility, usability, safety and technical performance of the device is investigated through a clinical study, which took place at a rehabilitation center in Greece involving real patients with mild to moderate cognitive and mobility impairment. This first evaluation study comprised a set of scenarios in a number of pre-defined use cases, including physical rehabilitation exercises, as well as mobility and ambulation involved in typical daily living activities of the patients. The design and implementation of this study is discussed in detail, along with the obtained results, which include both an objective and a subjective evaluation of the system operation, based on a set of technical performance measures and a validated questionnaire for the analysis of qualitative data, respectively. The study shows that the technical modules performed satisfactory under real conditions, and that the users generally hold very positive views of the platform, considering it safe and reliable.
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Affiliation(s)
- George Moustris
- School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), Athens, Greece
| | - Nikolaos Kardaris
- School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), Athens, Greece
| | - Antigoni Tsiami
- School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), Athens, Greece
| | - Georgia Chalvatzaki
- School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), Athens, Greece
| | - Petros Koutras
- School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), Athens, Greece
| | - Athanasios Dometios
- School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), Athens, Greece
| | - Paris Oikonomou
- School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), Athens, Greece
| | - Costas Tzafestas
- School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), Athens, Greece
| | - Petros Maragos
- School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), Athens, Greece
| | - Eleni Efthimiou
- Embodied Interaction and Robotics Department, Institute for Language and Speech Processing, ATHENA RC, Maroussi, Greece
| | - Xanthi Papageorgiou
- Embodied Interaction and Robotics Department, Institute for Language and Speech Processing, ATHENA RC, Maroussi, Greece
| | - Stavroula-Evita Fotinea
- Embodied Interaction and Robotics Department, Institute for Language and Speech Processing, ATHENA RC, Maroussi, Greece
| | - Yiannis Koumpouros
- Embodied Interaction and Robotics Department, Institute for Language and Speech Processing, ATHENA RC, Maroussi, Greece
- University of West Attica, Athens, Greece
| | - Anna Vacalopoulou
- Embodied Interaction and Robotics Department, Institute for Language and Speech Processing, ATHENA RC, Maroussi, Greece
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