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1.
Sensors (Basel) ; 22(16)2022 Aug 18.
Article in English | MEDLINE | ID: mdl-36015951

ABSTRACT

Sleep plays a critical role in stroke recovery. However, there are limited practices to measure sleep for individuals with stroke, thus inhibiting our ability to identify and treat poor sleep quality. Wireless, body-worn sensors offer a solution for continuous sleep monitoring. In this study, we explored the feasibility of (1) collecting overnight biophysical data from patients with subacute stroke using a simple sensor system and (2) constructing machine-learned algorithms to detect sleep stages. Ten individuals with stroke in an inpatient rehabilitation hospital wore two wireless sensors during a single night of sleep. Polysomnography served as ground truth to classify different sleep stages. A population model, trained on data from multiple patients and tested on data from a separate patient, performed poorly for this limited sample. Personal models trained on data from one patient and tested on separate data from the same patient demonstrated markedly improved performance over population models and research-grade wearable devices to detect sleep/wake. Ultimately, the heterogeneity of biophysical signals after stroke may present a challenge in building generalizable population models. Personal models offer a provisional method to capture high-resolution sleep metrics from simple wearable sensors by leveraging a single night of polysomnography data.


Subject(s)
Stroke Rehabilitation , Stroke , Wearable Electronic Devices , Humans , Polysomnography/methods , Sleep
2.
NPJ Digit Med ; 2: 131, 2019.
Article in English | MEDLINE | ID: mdl-31886412

ABSTRACT

Polysomnography (PSG) is the current gold standard in high-resolution sleep monitoring; however, this method is obtrusive, expensive, and time-consuming. Conversely, commercially available wrist monitors such as ActiWatch can monitor sleep for multiple days and at low cost, but often overestimate sleep and cannot differentiate between sleep stages, such as rapid eye movement (REM) and non-REM. Wireless wearable sensors are a promising alternative for their portability and access to high-resolution data for customizable analytics. We present a multimodal sensor system measuring hand acceleration, electrocardiography, and distal skin temperature that outperforms the ActiWatch, detecting wake and sleep with a recall of 74.4% and 90.0%, respectively, as well as wake, non-REM, and REM with recall of 73.3%, 59.0%, and 56.0%, respectively. This approach will enable clinicians and researchers to more easily, accurately, and inexpensively assess long-term sleep patterns, diagnose sleep disorders, and monitor risk factors for disease in both laboratory and home settings.

3.
J Neuroeng Rehabil ; 15(1): 19, 2018 03 13.
Article in English | MEDLINE | ID: mdl-29534737

ABSTRACT

BACKGROUND: Monitoring physical activity and leveraging wearable sensor technologies to facilitate active living in individuals with neurological impairment has been shown to yield benefits in terms of health and quality of living. In this context, accurate measurement of physical activity estimates from these sensors are vital. However, wearable sensor manufacturers generally only provide standard proprietary algorithms based off of healthy individuals to estimate physical activity metrics which may lead to inaccurate estimates in population with neurological impairment like stroke and incomplete spinal cord injury (iSCI). The main objective of this cross-sectional investigation was to evaluate the validity of physical activity estimates provided by standard proprietary algorithms for individuals with stroke and iSCI. Two research grade wearable sensors used in clinical settings were chosen and the outcome metrics estimated using standard proprietary algorithms were validated against designated golden standard measures (Cosmed K4B2 for energy expenditure and metabolic equivalent and manual tallying for step counts). The influence of sensor location, sensor type and activity characteristics were also studied. METHODS: 28 participants (Healthy (n = 10); incomplete SCI (n = 8); stroke (n = 10)) performed a spectrum of activities in a laboratory setting using two wearable sensors (ActiGraph and Metria-IH1) at different body locations. Manufacturer provided standard proprietary algorithms estimated the step count, energy expenditure (EE) and metabolic equivalent (MET). These estimates were compared with the estimates from gold standard measures. For verifying validity, a series of Kruskal Wallis ANOVA tests (Games-Howell multiple comparison for post-hoc analyses) were conducted to compare the mean rank and absolute agreement of outcome metrics estimated by each of the devices in comparison with the designated gold standard measurements. RESULTS: The sensor type, sensor location, activity characteristics and the population specific condition influences the validity of estimation of physical activity metrics using standard proprietary algorithms. CONCLUSIONS: Implementing population specific customized algorithms accounting for the influences of sensor location, type and activity characteristics for estimating physical activity metrics in individuals with stroke and iSCI could be beneficial.


Subject(s)
Algorithms , Spinal Cord Injuries/rehabilitation , Stroke Rehabilitation , Wearable Electronic Devices , Adult , Cross-Sectional Studies , Energy Metabolism/physiology , Exercise/physiology , Female , Humans , Male , Middle Aged , Pilot Projects , Spinal Cord Injuries/metabolism , Spinal Cord Injuries/physiopathology , Stroke/metabolism , Stroke/physiopathology
4.
Prog Brain Res ; 190: 3-20, 2011.
Article in English | MEDLINE | ID: mdl-21531242

ABSTRACT

Circadian rhythms in physiology and behavior exist in all living organisms, from cells to humans. The most evident rhythms are the recurrent cycles of sleep and wake as well as changes in alertness and cognitive performance across the 24h. Clearly, sleep pressure can exert a strong influence on cognitive performance, but the influence of circadian modulation of alertness and cognitive function is evident even when the pressure for sleep is high. Circadian rhythms also influence more complex cognitive tasks, such as selective attention and executive function, which are important for work performance and safety. The circadian timekeeping system also ensures that circadian rhythms are appropriately synchronized to the external physical environment and work and social schedules. Circadian misalignment is the basis for all circadian rhythm sleep disorders. These disorders are often associated with impairments of cognitive performance that can have adverse effects on school and work performance, overall quality of life, and safety.


Subject(s)
Circadian Rhythm/physiology , Cognition/physiology , Sleep Disorders, Circadian Rhythm/physiopathology , Sleep/physiology , Biological Clocks/physiology , Humans , Jet Lag Syndrome , Light , Melatonin/metabolism , Psychomotor Performance/physiology , Quality of Life , Wakefulness/physiology , Work Schedule Tolerance
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