nxtHealth WEARBLS incorporate sophisticated sensors and processing to go beyond consumer-level devices such as smart watches.
nxtHealth WEARBLS Internet of Things (IOT)-enabled, Physiological Sensors
nxtHealth WEARBLS provide new tools for disease management and improve the quality of care by giving clinicians unprecedented visibility into their patients’ activities, environment, and physiological factors. Furthermore, nxtHealth WEARBLS incorporate sophisticated sensors and processing to go beyond consumer-level devices such as smart watches. The sensors integrate with corresponding apps and analytics and are suited for use in both research and clinical environments. This data syncs with a nxtHealth APP or ANALYTX web portal via Bluetooth low energy (BLE).
Light & Actigraphy
DeLux is a lightweight sensor that monitors light exposure and actigraphy. The DeLux sensors measure light exposure using white light as well as three primary color wavelengths. DeLux converts light intensity to lux, irradiance, color temperature, or circadian light. Furthermore, DeLux measures activity using a three-axis accelerometer to compute an actigraphy index. This data computes activity indices and detects sleep patterns.
The user wears a small, discrete sensor resembling a small lapel pin and a wristwatch. These sensors are non-obtrusive to the wearer’s activities. nxtHealth WEARBLS share data via Bluetooth and stores the data for analysis on nxtHealth’s cloud-based ANALYTX platform.
Additionally, DeLux sensors measure and store data for up to 12 weeks using a single coin cell battery. Configurable sensor parameters include power state, sample rate, sensor averaging rate, and Bluetooth synchronization frequency.
Gait & Balance
Adrestia is a ruggedized, compact, wearable inertial measurement unit (IMU) that senses the wearer’s gait and balance. Designed for use in field hospitals and other austere environments that lack tools, the tool measures sensorimotor issues.
Adrestia captures data on key measures such as static postural sway (very low-frequency movements) and gait initiation during running and highly dynamic agility drills that include rapid turns as well as dual and multitasking activities. This type of data capture represents a leap forward over the current practice in which assessments, e.g., the Balance Error Scoring System (BESS) and the Functional Gait Assessment (FGA) rely heavily on subjective visual inspection and interpretations that may be prone to errors even when conducted by expert clinicians.