Remote Physical Frailty Monitoring– The Application of Deep Learning-Based Image Processing in Tele-Health

Zahiri, Mohsen; Wang, Changhong; Gardea, Manuel; Nguyen, Huan Buu; Shahbazi, Mohammad; Sharafkhaneh, Amir; Ruiz, Ilse Torres; Nguyen, Christina; Bryant, Monthaporn S.; Najafi, Bijan

doi:10.1109/access.2020.3042451

Cited by 21 publications

(13 citation statements)

References 28 publications

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“…One such implementation, a sensor-less deep learning image-processing frailty meter [ 36 ] records 20 s elbow movement videos (flexion and extension) through the camera of a tablet and then the frailty phenotype and frailty index are calculated, thus screening the patient for physical frailty and having the means to triage patients with chronic obstructive pulmonary disease (COPD).…”

Section: Overview Of Healthcare Applicationsmentioning

confidence: 99%

A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence

Pap

Oniga

2022

IJERPH

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Over the last couple of years, in the context of the COVID-19 pandemic, many healthcare issues have been exacerbated, highlighting the paramount need to provide both reliable and affordable health services to remote locations by using the latest technologies such as video conferencing, data management, the secure transfer of patient information, and efficient data analysis tools such as machine learning algorithms. In the constant struggle to offer healthcare to everyone, many modern technologies find applicability in eHealth, mHealth, telehealth or telemedicine. Through this paper, we attempt to render an overview of what different technologies are used in certain healthcare applications, ranging from remote patient monitoring in the field of cardio-oncology to analyzing EEG signals through machine learning for the prediction of seizures, focusing on the role of artificial intelligence in eHealth.

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Section: Overview Of Healthcare Applicationsmentioning

confidence: 99%

A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence

Pap

Oniga

2022

IJERPH

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“…Sensitivity was found to be high in monitoring physical frailty in both groups using the sensor-based system and camera-based telehealth system. It was seen that physical fragility could be monitored remotely by using a twodimensional and high-resolution camera (16).…”

Section: Remote Patient Monitoring (Monitorization)mentioning

confidence: 99%

Telemedicine/telehealth in Preventive Medicine

Yıldırım¹,

Uzun²,

Egici³

2022

acmr

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Telemedicine describes the remote medical support that the patients receive from physicians for their health. Telehealth, on the other hand, covers all subjects such as patient education, diagnosis, treatment, raising awareness about health issues, and includes mobile health as well as telemedicine. Preventive medicine describes practices and approaches to prevent undesirable situations and complications before, during, or after the occurrence of diseases.Primary care providers, who practice preventive medicine most intensively, also frequently exercise telehealth practices. Costeffectiveness, data security, addressing the lack of education of patients and doctors, redesigning computer systems in terms of ease of use, making necessary legal regulations, and simplifying bureaucratic duties create concerns for physicians and users.Since physical contact with the patient is not possible, communication between the doctor and the patient should be maintained simultaneously through technological methods. Therefore, the existing infrastructure, internet network, and hospital data system should be both fast, and near-perfect. Telemedicine may be a very good alternative for patients, assuming that healthcare institutions are largely concerned with pandemic patients, or that the risk of Covid-19 has not yet been eliminated in hospitals.

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“…This innovative FM requires only one wearable sensor attached to the wrist to measure the forearm and elbow joint motion during a 20-s rapid elbow flexion and extension test. The FM quantifies slowness, rigidity, weakness, and exhaustion by measuring comprehensive functional performance metrics includ-Gerontology 2022;68:829-839 DOI: 10.1159/000520401 ing elbow angular velocity, elbow angular acceleration, elbow flexion-extension range, time to reach full elbow flexion, and time to reach full elbow extension [19]. Our previous studies have demonstrated this FM is efficient in identifying individuals with pre-frailty and frailty [29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

“…Evidence has documented that the presence of frailty in elderly and certain chronic-disease population is associated with increased mortality, postoperative complications, prolonged hospitalization, and poor discharge disposition [14, 15, 18]. Therefore, frailty assessment has received considerable attention to predict outcomes and track recovery postintervention in older adults and in particular among those with chronic illness including COPD patients [10, 19]. The most popular method for physical frailty assessment is the frailty phenotype proposed by Fried et al [20], which assesses 5 physical components: unintentional weight loss (assessed by a questionnaire), slowness (assessed by a handgrip strength test), weakness (assessed by a 4.57-m walking test), exhaustion (assessed by a questionnaire), and low physical activity (assessed by a questionnaire).…”

Section: Introductionmentioning

confidence: 99%

Harnessing Digital Health to Objectively Assess Functional Performance in Veterans with Chronic Obstructive Pulmonary Disease

Zhou¹,

Park²,

Poursina³

et al. 2021

Gerontology

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Introduction: An early detection of impaired functional performance is critical to enhance symptom management for patients with chronic obstructive pulmonary disease (COPD). However, conventional functional measures based on walking assessments are often impractical for small clinics where the available space to administrate gait-based test is limited. This study examined the feasibility and effectiveness of an upper-extremity frailty meter (FM) in identifying digital measures of functional performance and assessing frailty in COPD patients. Methods: Forty-eight patients with COPD (age = 68.8 ± 8.5 years, body mass index [BMI] = 28.7 ± 5.8 kg/m2) and 49 controls (age = 70.0 ± 3.0 years, BMI = 28.7 ± 6.1 kg/m2) were recruited. All participants performed a 20-s repetitive elbow flexion-extension test using a wrist-worn FM sensor. Functional performance was quantified by FM metrics, including speed (slowness), range of motion (rigidity), power (weakness), flexion and extension time (slowness), as well as speed and power reduction (exhaustion). Conventional functional measures, including timed-up-and-go test, gait and balance tests, and 5 repetition sit-to-stand test, were also performed. Results: Compared to controls, COPD patients exhibited deteriorated performances in all conventional functional assessments (d = 0.64–1.26, p < 0.010) and all FM metrics (d = 0.45–1.54, p < 0.050). FM metrics had significant agreements with conventional assessment tools (|r| = 0.35–0.55, p ≤ 0.001). FM metrics efficiently identified COPD patients with pre-frailty and frailty (d = 0.82–2.12, p < 0.050). Conclusion: This study proposes the feasibility of using a 20-s repetitive elbow flexion-extension test and wrist-worn sensor-derived frailty metrics as an alternative and practical solution to evaluate functional performance in COPD patients. Its simplicity and low risk for test administration may also facilitate its application for remote patient monitoring. Furthermore, in settings where the administration of walking test is impractical, for example, when ventilator support is needed or space is limited, FM may be used as an alternative solution. Future studies are encouraged to use the FM to quantitatively monitor the progressive decline in functional performance and quantify outcomes of rehabilitation interventions.

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Remote Physical Frailty Monitoring– The Application of Deep Learning-Based Image Processing in Tele-Health

Cited by 21 publications

References 28 publications

A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence

A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence

Telemedicine/telehealth in Preventive Medicine

Harnessing Digital Health to Objectively Assess Functional Performance in Veterans with Chronic Obstructive Pulmonary Disease

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