RehabPhone

Zhang, Hanbin; Guo, Gabriel; Comstock, Emery; Chen, Baicheng; Chen, Xingyu; Song, Chen; Ajay, Jerry; Langan, Jeanne; Bhattacharjya, Sutanuka; Cavuoto, Lora; Xu, Wenyao

doi:10.1145/3386901.3389028

Cited by 13 publications

(3 citation statements)

References 32 publications

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“…Some applications were developed for upper extremity rehabilitation. For example, studies in [18], [19], developed software systems in which mobile applications are coupled with objects generated by 3D printers, resulting in high efficacy in home-based upper limb rehabilitation. Rehabilitation treatment programs in mobile game-based virtual reality were shown to be effective in promoting the recovery of upper limb function in stroke patients [20], [21], [22].…”

Section: A Mobile Health Application For Stroke Rehabilitationmentioning

confidence: 99%

Automatic Assessment of Upper Extremity Function and Mobile Application for Self-Administered Stroke Rehabilitation

Kim,

Park,

Kim

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Rehabilitation training is essential for a successful recovery of upper extremity function after stroke. Training programs are typically conducted in hospitals or rehabilitation centers, supervised by specialized medical professionals. However, frequent visits to hospitals can be burdensome for stroke patients with limited mobility. We consider a self-administered rehabilitation system based on a mobile application in which patients can periodically upload videos of themselves performing reach-to-grasp tasks to receive recommendations for self-managed exercises or progress reports. Sensing equipment aside from cameras is typically unavailable in the home environment. A key contribution of our work is to propose a deep learningbased assessment model trained only with video data. As all patients carry out identical tasks, a fine-grained assessment of task execution is required. Our model addresses this difficulty by learning RGB and optical flow data in a complementary manner. The correlation between the RGB and optical flow data is captured by a novel module for modality fusion using cross-attention with Transformers.Experiments showed that our model achieved higher accuracy in movement assessment than existing methods for action recognition. Based on the assessment model,

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Section: A Mobile Health Application For Stroke Rehabilitationmentioning

confidence: 99%

Automatic Assessment of Upper Extremity Function and Mobile Application for Self-Administered Stroke Rehabilitation

Kim,

Park,

Kim

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…With ease in collecting this health information, patients can better personalize their care with their healthcare providers. The advent of 3D printing has also allowed for custom attachments to the phone that opens the door to other screening and treatment processes [48][49][50]. A study has shown the feasibility of using a 3D-printed attachment to the smartphone to perform blood clot testing [49].…”

Section: Plos Digital Healthmentioning

confidence: 99%

Telehealth and digital health innovations: A mixed landscape of access

Phuong,

Ordóñez,

Cao

et al. 2023

PLOS Digit Health

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In the wake of emergent natural and anthropogenic disasters, telehealth presents opportunities to improve access to healthcare when physical access is not possible. Yet, since the beginning of the COVID pandemic, lessons learned reveal that various populations in the United States do not or cannot adopt telehealth due to inequitable access. We explored the Digital Determinants of Health (DDoHs) for telehealth, characterizing the role of accessibility, broadband connectivity and electrical grids, and patient intersectionality. In addition to its role as an existing Social Determinant of Health, Policies and Laws directly and indirectly affect these DDoHs, making access more complex for marginalized populations. Digital systems lack the flexibility, accessibility, and usability to inclusively provide the essential services patients need in telehealth. We propose the following recommendations: (1) design technology and systems using accessibility and value sensitive design principles; (2) support a range of technologies and settings; (3) support multiple and diverse users; and (4) support clear paths for repair when technical systems fail to meet users’ needs. Addressing these requires change not only from providers but also from the institutions providing these systems.

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“…Since tactile sensitivity is known to decrease with age and/or with other conditions more prevalent in older populations (Stevens, 1992), handling 3D models might prove advantageous as it involves additional proprioceptive feedback (Norman et al, 2011). In practice, emerging projects involving older adults explore the use of AM to design simpler tactile maps with less reliance on abstract symbols (Palivcová et al, 2020), and improve telerehabilitation (Zhang et al, 2020), a now relevant need in the context of the COVID-19 global pandemic. Nevertheless, it is likely that numerous possible applications of AM are yet to be discussed and tested.…”

Section: Am For Communication Learning and Mobilitymentioning

confidence: 99%

Applications of Additive Manufacturing, or 3D Printing, in the Rehabilitation of Individuals With Deafblindness: A Scoping Study

et al. 2022

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Additive manufacturing (AM), also known as 3D printing, is a promising tool to produce assistive technology. For instance, individuals with deafblindness (concurrent vision and hearing loss) could benefit from tactile AM-based products as touch may be their main gateway to access information. This study thus aimed to synthesize evidence on the current and potential practices involving AM in the context of deafblindness rehabilitation and to inform healthcare professionals and family caregivers on how AM can improve functioning and quality of life. A comprehensive literature search of ten databases (PsycINFO, MEDLINE, Global Health, PubMed, CINAHL, EMBASE, ERIC, Web of Science, Engineering Village, and Scopus) was performed to identify sources focusing on the use of AM toward rehabilitation goals of individuals with deafblindness. Nine of 1,397 studies met the inclusion criteria. The findings reveal that AM can counter barriers to full accessibility by enabling professionals to produce customized adapted material and communication devices, thus assisting individuals with deafblindness in communication, mobility, and learning. However, this review highlights a need for more AM research, resources, and training: interdisciplinary collaborations with AM specialists thus appear essential in improving rehabilitation services with AM.

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RehabPhone

Cited by 13 publications

References 32 publications

Automatic Assessment of Upper Extremity Function and Mobile Application for Self-Administered Stroke Rehabilitation

Automatic Assessment of Upper Extremity Function and Mobile Application for Self-Administered Stroke Rehabilitation

Telehealth and digital health innovations: A mixed landscape of access

Applications of Additive Manufacturing, or 3D Printing, in the Rehabilitation of Individuals With Deafblindness: A Scoping Study

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