Potential corner case cautions regarding publicly available implementations of the National Cancer Institute's nonwear/wear classification algorithm for accelerometer data.

The National Cancer Institute's (NCI) wear time classification algorithm uses a rule based on the occurrence of physical activity data counts-a cumulative measure of movement, influenced by both magnitude and duration of acceleration-to differentiate between when a physical activity monitoring (PAM) device (ActiGraph accelerometer) is being worn by a participant (wear) from when it is not (nonwear). It was applied to PAM data generated from the 2003-2004 National Health and Nutrition Examination Survey (NHANES 2003-2004). We discuss two corner case conditions that can produce unexpected, and perhaps unintended results when the algorithm is applied. We show, using simulated data of two special cases, how this algorithm classifies a 24-hour period with only 72 total counts as 100% wear in one case, and classifies a 24-hour period with 96,000 counts as 0.1% wear in another. The prevalence of like scenarios in the NHANES 2003-2004 PAM dataset is presented with corresponding summary statistics for varying degrees of the algorithm's nonwear classification threshold (T). The number of participants with valid days, defined as 10 or more hours classified as wear time in a 24-hour day, increased while the mean counts-per-minute (CPM) decreased as the threshold for excluding non-wear was reduced from the allowed 4,000 counts in an hour. The number of participants with four or more valid days increased 2.29% (n = 113) and mean CPM dropped 2.45% (9.5 CPM) when adjusting the nonwear classification threshold to 50 counts an hour. Applying the most liberal criteria, only excluding hours as nonwear which contained 1 count or less, resulted in a 397 more participants (7.83% increase) and 26.5 fewer CPM (6.98% decrease) in NHANES 2003-2004 participants with four or more valid days. The algorithm should be used with caution due to the potential influence of these corner cases.

Sleep devices: wearables and nearables, informational and interventional, consumer and clinical.

The field of sleep is in many ways ideally positioned to take full advantage of advancements in technology and analytics that is fueling the mobile health movement. Combining hardware and software advances with increasingly available big datasets that contain scored data obtained under gold standard sleep laboratory conditions completes the trifecta of this perfect storm. This review highlights recent developments in consumer and clinical devices for sleep, emphasizing the need for validation at multiple levels, with the ultimate goal of using personalized data and advanced algorithms to provide actionable information that will improve sleep health.

Wearable technology for patients with brain and spinal cord injuries.

Studies have shown that patients who practice functional movements at home in conjunction with outpatient therapy show higher improvement in motor recovery. However, patients are not qualified to monitor or assess their own condition that must be reported back to the clinician. Therefore, there is a need to transmit physiological data to clinicians from patients in their home environment. This paper presents a review of wearable technology for in-home health monitoring, assessment, and rehabilitation of patients with brain and spinal cord injuries.

Wearable Haptic Systems for the Fingertip and the Hand: Taxonomy, Review, and Perspectives.

In the last decade, we have witnessed a drastic change in the form factor of audio and vision technologies, from heavy and grounded machines to lightweight devices that naturally fit our bodies. However, only recently, haptic systems have started to be designed with wearability in mind. The wearability of haptic systems enables novel forms of communication, cooperation, and integration between humans and machines. Wearable haptic interfaces are capable of communicating with the human wearers during their interaction with the environment they share, in a natural and yet private way. This paper presents a taxonomy and review of wearable haptic systems for the fingertip and the hand, focusing on those systems directly addressing wearability challenges. The paper also discusses the main technological and design challenges for the development of wearable haptic interfaces, and it reports on the future perspectives of the field. Finally, the paper includes two tables summarizing the characteristics and features of the most representative wearable haptic systems for the fingertip and the hand.

Stability of Enzymatic Biosensors for Wearable Applications.

Technological evolution in wearable sensors accounts for major growth and transformation in a multitude of industries, ranging from healthcare to computing and informatics to communication and biomedical sciences. The major driver for this transformation is the new-found ability to continuously monitor and analyze the patients' physiology in patients' natural setting. Numerous wearable sensors are already on the market and are summarized. Most of the current technologies have focused on electrophysiological, electromechanical, or acoustic measurements. Wearable biochemical sensing devices are in their infancy. Traditional challenges in biochemical sensing such as reliability, repeatability, stability, and drift are amplified in wearable sensing systems due to variabilities in operating environment, sample/sensor handling, and motion artifacts. Enzymatic sensing technologies, due to reduced fluidic challenges, continue to be forerunners for converting into wearable sensors. This paper reviews the recent developments in wearable enzymatic sensors. The wearable sensors have been classified in three major groups based on sensor embodiment and placement relative to the human body: 1) on-body, 2) clothing/textile-based biosensors, and 3) biosensor accessories. The sensors, which come in the forms of stickers and tattoos, are categorized as on-body biosensors. The fabric-based biosensor comes in different models such as smart-shirts, socks, gloves, and smart undergarments with printed sensors for continuous monitoring.