Ambulatory sleep scoring using accelerometers—distinguishing between nonwear and sleep/wake states

Background. Differentiating nonwear time from sleep and wake times is essential forthe estimation of sleep duration based on actigraphy data. To efficiently analyze large-scale data sets, an automatic method of identifying these three different states is re-quired. Therefore, we developed a classification algorithm to determine nonwear, sleepand wake periods from accelerometer data. Our work aimed to (I) develop a new patternrecognition algorithm for identifying nonwear periods from actigraphy data based onthe influence of respiration rate on the power spectrum of the acceleration signal andimplement it in an automatic classification algorithm for nonwear/sleep/wake states;(II) address motion artifacts that occur during nonwear periods and are known to causemisclassification of these periods; (III) adjust the algorithm depending on the sensorposition (wrist, chest); and (IV) validate the algorithm on both healthy individuals andpatients with sleep disorders. Methods. The study involved 98 participants who wore wrist and chest accelerationsensors for one day of measurements. They spent one night in the sleep laboratoryand continued to wear the sensors outside of the laboratory for the remainder of theday. The results of the classification algorithm were compared to those of the referencesource: polysomnography for wake/sleep and manual annotations for nonwear/wearclassification. Results. The median kappa values for the two locations were 0.83 (wrist) and 0.84(chest). The level of agreement did not vary significantly by sleep health (good sleepersvs. subjects with sleep disorders) (p=0.348,p=0.118) or by sex (p=0.442,p=0.456).The intraclass correlation coefficients of nonwear total time between the referenceand the algorithm were 0.92 and 0.97 with the outliers and 0.95 and 0.98 after theoutliers were removed for the wrist and chest, respectively. There was no evidence of anassociation between the mean difference (and 95% limits of agreement) and the meanof the two methods for either sensor position (wrist p=0.110, chest p=0.164), and themean differences (algorithm minus reference) were 5.11 [95% LoA−15.4–25.7] and1.32 [95% LoA−9.59–12.24] min/day, respectively, after the outliers were removed. Discussion. We studied the influence of the respiration wave on the power spectrum ofthe acceleration signal for the differentiation of nonwear periods from sleep and wakeperiods. The algorithm combined both spectral analysis of the acceleration signal and rescoring. Based on the Bland-Altman analysis, the chest-worn accelerometer showed better results than the wrist-worn accelerometer

A review of activity trackers for senior citizens: research perspectives, commercial landscape and the role of the insurance industry

The objective assessment of physical activity levels through wearable inertial-based motion detectors for the automatic, continuous and long-term monitoring of people in free-living environments is a well-known research area in the literature. However, their application to older adults can present particular constraints. This paper reviews the adoption of wearable devices in senior citizens by describing various researches for monitoring physical activity indicators, such as energy expenditure, posture transitions, activity classification, fall detection and prediction, gait and balance analysis, also by adopting consumer-grade fitness trackers with the associated limitations regarding acceptability. This review also describes and compares existing commercial products encompassing activity trackers tailored for older adults, thus providing a comprehensive outlook of the status of commercially available motion tracking systems. Finally, the impact of wearable devices on life and health insurance companies, with a description of the potential benefits for the industry and the wearables market, was analyzed as an example of the potential emerging market drivers for such technology in the future

Analyzing sensor based human activity data using time series segmentation to determine sleep duration

Sleep is the most important thing to rest our brain and body. A lack of sleep has adverse effects on overall personal health and may lead to a variety of health disorders. According to Data from the Center for disease control and prevention in the United States of America, there is a formidable increase in the number of people suffering from sleep disorders like insomnia, sleep apnea, hypersomnia and many more. Sleep disorders can be avoided by assessing an individual\u27s activity over a period of time to determine the sleep pattern and duration. The sleep pattern and duration can be determined for an individual with the help of commercially available fitness devices such as Fitbit, Nike, Apple, and many others, which are activity trackers with accelerometer sensors. But these devices determine sleep duration from a \u27Proprietary Algorithm\u27, which processes the movement sensor data. Due to the proprietary nature, in a long-term study, the developer of the algorithm could update and make changes to the algorithm without revealing the details of the update to the user. This affects the measures reported by the algorithm. Hence to determine correct and reliable sleep duration, an Algorithm is developed by directly analyzing the actigraphy signals using time series segmentation. The study was done on a group of 20 healthy Undergraduate students from Missouri University of Science and Technology, whose daily physical activities were recorded using the GENEActiv accelerometer wristwatch worn on the non-dominant wrist. In this thesis, an open source algorithm has been developed using the daily physical activity data to estimate the sleep duration for any individual --Abstract, page iii

Review of Wearable Devices and Data Collection Considerations for Connected Health

Wearable sensor technology has gradually extended its usability into a wide range of well-known applications. Wearable sensors can typically assess and quantify the wearer’s physiology and are commonly employed for human activity detection and quantified self-assessment. Wearable sensors are increasingly utilised to monitor patient health, rapidly assist with disease diagnosis, and help predict and often improve patient outcomes. Clinicians use various self-report questionnaires and well-known tests to report patient symptoms and assess their functional ability. These assessments are time consuming and costly and depend on subjective patient recall. Moreover, measurements may not accurately demonstrate the patient’s functional ability whilst at home. Wearable sensors can be used to detect and quantify specific movements in different applications. The volume of data collected by wearable sensors during long-term assessment of ambulatory movement can become immense in tuple size. This paper discusses current techniques used to track and record various human body movements, as well as techniques used to measure activity and sleep from long-term data collected by wearable technology devices

Sedentary time across the transition to retirement and after an activity tracker intervention

This study aimed to examine how accelerometer-measured daily total and prolonged sedentary time change across the transition to retirement using annual measurement data from the Finnish Retirement and Aging (FIREA) study (n=689). Another aim was to examine the effect of a 12-month activity tracker-based intervention on sedentary time among recent retirees using data from the REACT trial (n=231). The final aim was to compare sedentary time estimates of the wrist-worn accelerometers used in both studies to the estimates obtained by a more reliable method, a thighworn accelerometer. Daily total sedentary time only changed among women retiring from manual occupations. Their daily total sedentary time increased by 54 minutes immediately after the transition to retirement. Prolonged sedentary time increased by half an hour across gender and occupational groups. The timing of the changes in relation to retirement differed between genders, as women’s prolonged sedentary time increased immediately after the transition to retirement, whereas the increase in men’s prolonged sedentary time was more gradual from the last years at work to a few years after retirement. The activity tracker-based intervention targeted at the first years after retirement did not elicit changes in daily total or prolonged sedentary time over 12 months in comparison to the controls. The wrist-worn accelerometer either underestimated or overestimated daily total sedentary time in comparison to the thigh-worn accelerometer, depending on the method used. However, withinindividual differences in sedentary time were similarly captured by each method, suggesting that the observed changes in sedentary time across retirement and the intervention were reliable. This study indicates that interventions to reduce sedentary time may be the most effective when targeted at the first years after retirement among women, but that the benefit for men may be highest during the last years in work life. As an activity tracker alone was insufficient to reduce sedentary time in the long term, other approaches or additional intervention components may be needed to attain long-term changes in sedentary timePaikallaanolo eläkkeelle siirryttäessä ja aktiivisuusintervention jälkeen Tämän tutkimuksen tavoitteena oli selvittää, miten liikemittarilla mitattu päivän paikallaanoloaika ja pitkittynyt paikallaanolo muuttuvat eläköidyttäessä käyttäen Finnish Retirement and Aging (FIREA)-tutkimuksen vuosittain toistettuja mittauksia (n=689). Tavoitteena oli myös tutkia, onko aktiivisuusrannekkeella vaikutusta paikallaanoloon juuri eläköityneillä henkilöillä käyttäen koe- ja kontrolliasetelmaa REACT-interventiotutkimuksen aineistossa (n=231). Tutkimuksessa mitattiin paikallaanoloa ranteessa pidettävällä liikemittarilla, minkä vuoksi tavoitteena oli myös vertailla rannemittarilla saatuja paikallaanolon estimaatteja reisimittarilla saatuihin luotettavampiin paikallaanolon estimaatteihin. Päivän kokonaispaikallaanolo muuttui ainoastaan naisilla, jotka eläköityivät fyysisistä ja palveluammateista. He lisäsivät paikallaanoloaikaansa 54 minuutilla päivässä heti eläköitymisen jälkeen. Pitkittynyt paikallaanolo lisääntyi sen sijaan noin puolella tunnilla päivässä sekä naisilla että miehillä ammatista riippumatta. Muutosten ajoittuminen eläköitymiseen nähden erosi naisten ja miesten välillä, koska naisilla pitkittyneen paikallaanoloajan lisääntyminen tapahtui heti eläköitymisen jälkeen, kun taas miehet lisäsivät pitkittynyttä paikallaanoloa tasaisesti viimeisistä työvuosista ensimmäisiin eläkevuosiin. Eläköitymisen jälkeiselle ajalle kohdistettu vuoden kestoinen aktiivisuusrannekeinterventio ei saanut aikaan muutoksia kokonaispaikallaanoloajassa eikä pitkittyneessä paikallaanoloajassa kontrolliryhmään nähden. Verrattuna reisimittariin, rannemittarilla saadut tulokset joko aliarvioivat tai yliarvioivat paikallaanoloa kiihtyvyysmittaridatan prosessointimenetelmästä riippuen. Havaitut paikallaanoloajan muutokset olivat kuitenkin samanlaisia menetelmästä riippumatta, joten paikallaanolon muutoksia koskevat tulokset ovat todennäköisesti luotettavia. Tämän tutkimuksen tulokset osoittavat, että interventiot paikallaanolon vähentämiseksi ovat perusteltuja pian eläköitymisen jälkeen naisilla, kun taas miehillä suurimmat hyödyt voidaan mahdollisesti saavuttaa jo viimeisinä työvuosina. Aktiivisuusrannekkeen käyttö ei riittänyt vähentämään paikallaanoloa pitkällä aikavälillä, minkä vuoksi rannekkeen lisäksi voidaan tarvita muita keinoja

Wearable Inertial Devices in Duchenne Muscular Dystrophy: A Scoping Review

In clinical practice and research, innovative digital technologies have been proposed for the characterization of neuromuscular and movement disorders through objective measures. Among these, wearable devices prove to be a suitable solution for tele-monitoring, tele-rehabilitation, and daily activities monitoring. Inertial Measurement Units (IMUs) are low-cost, compact, and easy-to-use wearable devices that evaluate kinematics during different movements. Kinematic variables could support the clinical evaluation of the progression of some neuromuscular diseases and could be used as outcome measures. The current review describes the use of IMUs for the biomechanical assessment of meaningful outcome measures in individuals affected by Duchenne muscular dystrophy (DMD). The PRISMA methodology was used and the search was conducted in different databases (Scopus, Web of Science, PubMed). A total of 23 articles were examined and classified according to year of publication, ambulatory/non-ambulatory subjects, and IMU positioning on human body. The analysis points out the recent regulatory identification of Stride Velocity 95th Centile as a new endpoint in therapeutic DMD trials when measured continuously from a wearable device, while only a few studies proposed the use of IMUs in non-ambulatory patients. Clinical recognition of reliable and accurate outcome measures for the upper body is still a challeng