Review of Wearable Devices and Data Collection Considerations for Connected Health

Abstract

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.

Technical, Regulatory, Economic, and Trust Issues Preventing Successful Integration of Sensors into the Mainstream Consumer Wearables Market

“…This is an important limitation since wearables are designed specifically for use outside the laboratory. Establishing ecological validity requires fit-for-purpose study design and testing within the target population [ 44 ]. For sensors that are designed to identify an activity state, such as sleep or exercise, devices can be compared against the wearer’s self-report.…”

“…Validation testing for IBR’s purpose-built sleep tracker not only included a comparison against the gold-standard PSG, but a subsequent comparison of all-day sleep measurement against self-reported sleep behavior in a population of long-haul pilots [ 12 , 45 ]. Self-report is commonly used to collect data about individuals in the real world, but is not always reliable [ 44 , 45 , 46 , 47 ]. Comparing a sensor or wearable’s ability to estimate activity states in real-world environments against self-report is better than no testing at all.…”

“…Establishing validity for biological signals that cannot be self-reported, such as HR, requires fit-for-purpose study design and testing within the target population [ 44 ]. For example, to test the accuracy of PPG HR sensors across a spectrum of activities and skin tones, the Department of Biomedical Engineering at Duke University recruited participants from diverse racial backgrounds, and gauged skin tone using the Fitzpatrick skin tone scale [ 26 , 48 ].…”

State-of-the-Art Sensors Research in Ireland

“…In [18], Vijayan et al give a review of wearable devices and data-collection considerations for connected health. This review examined various wearable devices used for quantified self, clinical assessments and automated monitoring of activity and sleep patterns.…”

SensorBadge: An Exploratory Study of an Ego-centric Wearable Sensor System for Healthy Office Environments

“…The weight can therefore be easily lowered by using a smaller, less heavy battery. However, lowering the battery will decrease the battery life of the artifact, potentially leading to a data loss, when forgetting to charge, and a higher efort from users [67]. A challenge is set here to fnd a balance between usability and wearability where users can use seamlessly use the SensorBadge without creating additional eforts [24].…”