Integrating AI-driven wearable devices and biometric data into stroke risk assessment: A review of opportunities and challenges

Clinical Neurology and Neurosurgery, Journal Year: 2024, Volume and Issue: 249, P. 108689 - 108689

Published: Dec. 10, 2024

Stroke is a leading cause of morbidity and mortality worldwide, early detection risk factors critical for prevention improved outcomes. Traditional stroke assessments, relying on sporadic clinical visits, fail to capture dynamic changes in such as hypertension atrial fibrillation (AF). Wearable technology (devices), combined with biometric data analysis, offers transformative approach by enabling continuous monitoring physiological parameters. This narrative review was conducted using systematic identify analyze peer-reviewed articles, reports, case studies from reputable scientific databases. The search strategy focused articles published between 2010 till date pre-determined keywords. Relevant were selected based their focus wearable devices AI-driven technologies prevention, diagnosis, rehabilitation. literature categorized thematically explore applications, opportunities, challenges, future directions. explores the current landscape assessment, focusing role detection, personalized care, integration into practice. highlights opportunities presented predictive analytics, where algorithms can provide tailored interventions. Personalized powered machine learning, enable individualized care plans. Furthermore, telemedicine facilitates remote patient rehabilitation, particularly underserved areas. Despite these advances, challenges remain. Issues accuracy, privacy concerns, wearables healthcare systems must be addressed fully realize potential. As evolves, its application could revolutionize improving outcomes reducing global burden stroke.

Language: Английский

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Patient performance assessment methods for upper extremity rehabilitation in assist-as-needed therapy strategies: a comprehensive review

Medical & Biological Engineering & Computing, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 7, 2025

This paper aims to comprehensively review patient performance assessment (PPA) methods used in assist-as-needed (AAN) robotic therapy for upper extremity rehabilitation. AAN strategies adjust assistance according the patient's performance, aiming enhance engagement and recovery individuals with motor impairments. categorizes implemented PPA literature first time such a wide scope suggests future research directions improve adaptive personalized therapy. At first, studies are examined evaluate methods, which subsequently categorized their underlying implementation strategies: position error-based force-based electromyography (EMG), electroencephalography (EEG)-based indicator-based physiological signal-based methods. The advantages limitations of each method discussed. In addition classification current study also examines clinically tested applied rehabilitation clinical outcomes. Clinical findings from these trials demonstrate potential improving function engagement. Nevertheless, more extensive testing is necessary establish long-term benefits over conventional therapies. Ultimately, this guide developments field rehabilitation, providing researchers insights into optimizing enhanced

Language: Английский

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Artificial intelligence in stroke rehabilitation: From acute care to long-term recovery

Neuroscience, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

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Functional and Motoric Outcome of AI-Assisted Stroke Rehabilitation: A Meta-analysis of Randomized Controlled Trials

Brain Disorders, Journal Year: 2025, Volume and Issue: unknown, P. 100224 - 100224

Published: April 1, 2025

Language: Английский

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Adoption of Artificial Intelligence in Rehabilitation: Perceptions, Knowledge, and Challenges Among Healthcare Providers

Healthcare, Journal Year: 2025, Volume and Issue: 13(4), P. 350 - 350

Published: Feb. 7, 2025

The current literature reveals a gap in understanding how rehabilitation professionals, such as physical and occupational therapists, perceive prepare to implement artificial intelligence (AI) their practices. Therefore, we conducted cross-sectional observational study assess the perceptions, knowledge, willingness of healthcare providers AI practice. This was Saudi Arabia, with data collected from 430 therapy professionals via an online SurveyMonkey questionnaire between January March 2024. survey assessed demographics, knowledge skills, perceived challenges. Data were analyzed using Statistical Package for Social Science (SPSS 27) DATAtab (version 2025), frequencies, percentages, nonparametric tests used examine relationships variables. majority respondents (80.9%) believed that would be integrated into future, 78.6% seeing significantly impacting work. While 61.4% thought reduce workload enhance productivity, only 30% expressed concerns about endangering profession. A lack formal training has commonly been reported, social media platforms being respondents' primary source knowledge. Despite these challenges, 85.1% eagerness learn use AI. Organizational preparedness significant barrier, 45.6% reporting organizations lacked strategies. There insignificant differences mean rank perceptions or based on gender, years experience, qualification degree respondents. results demonstrated strong interest implementation therapy. confidence AI's future utility readiness incorporate it However, organizational preparedness, identified. Overall, findings highlight potential revolutionize while underscoring necessity address fully realize this potential.

Language: Английский

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Effects of Artificial Intelligence Rehabilitation on Motor ability and Daily living ability of Hemiplegic Patients with Stroke—Meta-Analysis of Randomized Controlled Trials (Preprint)

Published: Feb. 10, 2025

A large number of hemiplegic stroke patients worldwide require rehabilitation. Artificial intelligence (AI) has the potential to conserve human resources and offers broad application prospects. With advancements in medicine technology, AI begun integrating into rehabilitation, providing personalized rehabilitation plans. However, effects on motor daily living abilities remain unclear. Evaluate patients. The Cochrane Library, Web Science, PubMed, Embase, CINAHL, CNKI, VIP, Wanfang databases were systematically searched for randomized controlled trials (RCTs) with stroke. search timeframe was from construction database January 1, 2025. literature screened according nerfing criteria, relevant information extracted, Meta-analysis performed using RevMan5.3 software. 16 studies involving 565 hemiplegia included. showed that, compared conventional more effective improving ability [MD=3.35, 95%CI (1.39, 5.32), P<0.001], balance [MD=7.26, (6.37, 8.14), muscle strength grip [SMD=0.65, (0.25, 1.04), P=0.001], perform activities [SMD=1.71, (0.73, 2.69), P<0.001]. improvements limb function [MD=0.11, (-0.06, 0.28), P=0.210], tone [MD=-0.28, (-0.57, 0.02), P=0.060], [MD=-0.04, (-0.49, 0.41), P=0.860], hand dexterity [MD=9.31, (-7.48, 26.09), P=0.280] not statistically significant. Subgroup analyses revealed no statistical difference between machines [MD=1.80, (-1.37, 4.97), P=0.270]. In contrast, virtual reality [MD=5.07, (4.23, 5.91), brain-computer interface [MD=6.99, (3.06, 10.92), telerehabilitation [MD=0.96, (0.23, 1.68), P=0.010] all significantly improved performance. Additionally, interventions a total frequency ≥20 [MD=4.29, (2.21, 6.36), P<0.001] duration ≥6 weeks [MD=3.73, (1.22, 6.24), P=0.004] effective. intervention ≥10 hours [MD=5.71, (3.02, 8.40), also had better effect improvement. that >10 [SMD=3.18, (1.44, 4.93), ability. can improve hemiplegia. Using reality, interface, is recommended, ,with interventions, hours. activities, recommended enhance function, strength, strength. it does function. be More high-quality are needed validate these findings further. PROSPERO CRD42025636225；https://tinyurl.com/2uc3eac2.

Language: Английский

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Better Understanding Rehabilitation of Motor Symptoms: Insights from the Use of Wearables

Pragmatic and Observational Research, Journal Year: 2025, Volume and Issue: Volume 16, P. 67 - 93

Published: March 1, 2025

Movement disorders present a substantial challenge by adversely affecting daily routines and overall well-being through diverse spectrum of motor symptoms. Traditionally, symptoms have been evaluated manual observational methods patient-reported outcomes. While those approaches are valuable, they limited their subjectivity. In contrast, wearable technologies (wearables) provide objective assessments while actively supporting rehabilitation continuous tracking, real-time feedback, personalized physical therapy-based interventions. The aim this literature review is to examine current research on the use wearables in symptoms, focusing features, applications, impact improving function. By exploring protocols, metrics, study findings, aims comprehensive overview how being used support optimize To achieve that aim, systematic search was conducted. Findings reveal gait disturbance postural balance primary extensively studied with tremor freezing (FoG) also receiving attention. Wearable sensing ranges from bespoke inertial and/or electromyography commercial units such as personal devices (ie, smartwatch). Interactive (virtual reality, VR augmented AR) immersive (headphones), along robotic systems (exoskeletons), proven be effective skills. Auditory cueing (via smartwatches or headphones), aids training rhythmic visual cues AR glasses) enhance exercises feedback. development treatment protocols incorporate via could adherence engagement potentially lead long-term improvements. However, evidence sustained effectiveness wearable-based interventions remains limited.

Language: Английский

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Technology Acceptance and Usability of a Therapy System with a Humanoid Robot Serving as Therapeutic Assistant for Post-Stroke Arm and Neurovisual Rehabilitation—An Evaluation Based on Stroke Survivors’ Experience

Biomimetics, Journal Year: 2025, Volume and Issue: 10(5), P. 289 - 289

Published: May 4, 2025

Background: This study performed an evaluation of technology acceptance the therapeutic system E-BRAiN (Evidence-Based Robot Assistance in Neurorehabilitation) by stroke survivors receiving therapy with system. Methods: The was based on a 49-item questionnaire addressing (I) its constituents, i.e., perceived usefulness, ease use, adaptability, enjoyment, attitude, trust, anxiety, social influence, sociability, and presence (41 items), (II) more general items exploring user experience terms both (3 items) usability (5 open-question items). Results: Eleven consecutive sub-acute who had received either arm rehabilitation sessions (n = 5) or neglect 6) led humanoid robot participated. multidimensional “strength acceptance” summary statistic (Part I) indicates high degree (mean, 4.0; 95% CI, 3.7 to 4.3p), as does “general 4.1; 3.3 4.9) (art II) (scores ranging from 1, lowest acceptance, 5, highest score 3 neutral anchor). Positive ratings were also documented for all assessed constituents I), well perception that it makes sense use supplement users’ own II). Conclusions: A functionality behaviour emotions while using system, could be corroborated among used E-BRAiN.

Language: Английский

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Artificial Intelligence and the Human–Computer Interaction in Occupational Therapy: A Scoping Review

Algorithms, Journal Year: 2025, Volume and Issue: 18(5), P. 276 - 276

Published: May 8, 2025

Occupational therapy (OT) is a client-centered health profession focused on enhancing individuals’ ability to perform meaningful activities and daily tasks, particularly for those recovering from injury, illness, or disability. As core component of rehabilitation, it promotes independence, well-being, quality life through personalized, goal-oriented interventions. Identifying measuring the role artificial intelligence (AI) in human–computer interaction (HCI) within OT critical improving therapeutic outcomes patient engagement. Despite AI’s growing significance, integration AI-driven HCI remains relatively underexplored existing literature. This scoping review identifies maps current research topic, highlighting applications proposing directions future work. A structured literature search was conducted using Scopus PubMed databases. Articles were included if their primary focus intersection AI, HCI, OT. Out 55 retrieved articles, 26 met inclusion criteria. work highlights three key findings: (i) machine learning, robotics, virtual reality are emerging as prominent techniques OT; (ii) AI-enhanced offers significant opportunities developing personalized interventions; (iii) further essential evaluate long-term efficacy, ethical implications, associated with These insights aim guide efforts clinical this evolving interdisciplinary field. In conclusion, holds considerable promise advancing practice, yet needed fully realize its potential.

Language: Английский

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AI-Driven Rehabilitation Robots: Enhancing Physical Therapy for Stroke and Injury Recovery

Next frontier., Journal Year: 2024, Volume and Issue: 8(1), P. 155 - 155

Published: Nov. 25, 2024

AI-driven rehabilitation robots are transforming physical therapy by providing personalized, precise, and adaptive support for patients recovering from strokes injuries. This research explores the integration of Artificial Intelligence (AI) into robotic systems to enhance outcomes, focusing on key areas such as motor skill recovery, real-time performance tracking, patient engagement. Utilizing machine learning algorithms biomechanical data, these can tailor sessions individual needs, dynamically adjusting resistance, movement patterns, feedback. Advanced sensor technology enables monitor progress, ensuring accurate assessments interventions. study also examines role AI in promoting neuroplasticity through repetitive, task-specific training, a critical component stroke recovery. Ethical considerations, including data privacy accessibility, analyzed address barriers widespread adoption. By bridging robotics, AI, clinical practice, this highlights potential revolutionize therapy, offering scalable effective solutions that improve recovery rates quality care.

Language: Английский

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