Monitoring physical activity with wearable technologies

Tokuçoğlu, Figen

doi:10.29399/npa.23333

Cited by 14 publications

(10 citation statements)

References 17 publications

(25 reference statements)

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“…Wearable sensor technologies for monitoring daily physical activity and health-related signals (e.g., heart rate, calorie burn, and blood oxygen level) continue to evolve, and their use is widespread (see [ 41 , 42 ] for review). Clearly, wearable sensor technologies have been accepted by the health care industry [ 43 ].…”

Section: Discussionmentioning

confidence: 99%

Digital Biomarkers of Physical Frailty and Frailty Phenotypes Using Sensor-Based Physical Activity and Machine Learning

Park

Mishra

Golledge

et al. 2021

Sensors

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Remote monitoring of physical frailty is important to personalize care for slowing down the frailty process and/or for the healthy recovery of older adults following acute or chronic stressors. Taking the Fried frailty criteria as a reference to determine physical frailty and frailty phenotypes (slowness, weakness, exhaustion, inactivity), this study aimed to explore the benefit of machine learning to determine the least number of digital biomarkers of physical frailty measurable from a pendant sensor during activities of daily living. Two hundred and fifty-nine older adults were classified into robust or pre-frail/frail groups based on the physical frailty assessments by the Fried frailty criteria. All participants wore a pendant sensor at the sternum level for 48 h. Of seventeen sensor-derived features extracted from a pendant sensor, fourteen significant features were used for machine learning based on logistic regression modeling and a recursive feature elimination technique incorporating bootstrapping. The combination of percentage time standing, percentage time walking, walking cadence, and longest walking bout were identified as optimal digital biomarkers of physical frailty and frailty phenotypes. These findings suggest that a combination of sensor-measured exhaustion, inactivity, and speed have potential to screen and monitor people for physical frailty and frailty phenotypes.

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Section: Discussionmentioning

confidence: 99%

Digital Biomarkers of Physical Frailty and Frailty Phenotypes Using Sensor-Based Physical Activity and Machine Learning

Park

Mishra

Golledge

et al. 2021

Sensors

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“…Recent advances in microelectronics have led to the production of small flexible sensors, even integrated into clothing ("e-textile") [138], thus making wearable devices suitable for free-living applications [139]. To date, the main wearable technologies available for balance assessment include mechanical devices, such as inertial and pressure sensors, and physiological devices, such as surface electromyography sensors (sEMG) (Figure 2).…”

Section: Wearable Technologiesmentioning

confidence: 99%

“…Currently, several wearable sensors, mostly including inertial devices, are available on the market for approved clinical use in balance assessment [154], also including self-adhesive biosensors (for further details see www.clinicaltrials.gov). The large volume of data produced by wearable sensors requires the development of specialised algorithms and machine learning algorithms to select clinically-valuable measures [138]. Owing to the considerable processing capacity of wearable devices, embedded algorithmic sets can be used for the online and remote execution, but at the expense of the battery charge duration.…”

Section: Wearable Technologiesmentioning

confidence: 99%

Fifteen Years of Wireless Sensors for Balance Assessment in Neurological Disorders

Zampogna

Mileti

Palermo

et al. 2020

Sensors

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Balance impairment is a major mechanism behind falling along with environmental hazards. Under physiological conditions, ageing leads to a progressive decline in balance control per se. Moreover, various neurological disorders further increase the risk of falls by deteriorating specific nervous system functions contributing to balance. Over the last 15 years, significant advancements in technology have provided wearable solutions for balance evaluation and the management of postural instability in patients with neurological disorders. This narrative review aims to address the topic of balance and wireless sensors in several neurological disorders, including Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, stroke, and other neurodegenerative and acute clinical syndromes. The review discusses the physiological and pathophysiological bases of balance in neurological disorders as well as the traditional and innovative instruments currently available for balance assessment. The technical and clinical perspectives of wearable technologies, as well as current challenges in the field of teleneurology, are also examined.

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“…Virtual run programmes propose objectively measured PA. In a real-world setting, virtual run participants can use personal mobile or wearable devices to objectively record their PA (38)(39)(40)(41). The real-time or final results of each participant are monitored.…”

Section: Advantagesmentioning

confidence: 99%

Perspectives on Using Online Platforms for Promoting Running and Walking Activities

Wattanapisit

Amaek

Sukkriang

et al. 2020

Front. Public Health

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Monitoring physical activity with wearable technologies

Cited by 14 publications

References 17 publications

Digital Biomarkers of Physical Frailty and Frailty Phenotypes Using Sensor-Based Physical Activity and Machine Learning

Digital Biomarkers of Physical Frailty and Frailty Phenotypes Using Sensor-Based Physical Activity and Machine Learning

Fifteen Years of Wireless Sensors for Balance Assessment in Neurological Disorders

Perspectives on Using Online Platforms for Promoting Running and Walking Activities

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