Development of a Smart Sleep Mask with Multiple Sensors.

In this work, we demonstrated a Smart Sleep Mask with several integrated physiological sensors such as 3-axis accelerometers, respiratory acoustic sensor, and an eye movement sensor. In particular, using infrared optical sensors, eye movement frequency, direction, and amplitude can be directly monitored and recorded during sleep sessions. We also developed a mobile app for data storage, signal processing and data analytics. Aggregation of these signals from a single wearable device may offer ease of use and more insights for sleep monitoring and REM sleep assessment. The user-friendly mask design can enable at-home use applications in the studies of digital biomarkers for sleep disorder related neurodegenerative diseases. Examples include REM Sleep Behavior Disorder, epilepsy event detection and stroke induced facial and eye movement disorder.Clinical Relevance-Many diseases such as stroke, epilepsy, and Parkinson's disease can cause significant abnormal events during sleep or are associated with sleep disorder. A smart sleep mask may serve as a simple platform to provide various physiological signals and generate clinical meaningful insights by revealing the neurological activities during various sleep stages.

Wearable Nail Deformation Sensing for Behavioral and Biomechanical Monitoring and Human-Computer Interaction.

The dynamics of the human fingertip enable haptic sensing and the ability to manipulate objects in the environment. Here we describe a wearable strain sensor, associated electronics, and software to detect and interpret the kinematics of deformation in human fingernails. Differential forces exerted by fingertip pulp, rugged connections to the musculoskeletal system and physical contact with the free edge of the nail plate itself cause fingernail deformation. We quantify nail warpage on the order of microns in the longitudinal and lateral axes with a set of strain gauges attached to the nail. The wearable device transmits raw deformation data to an off-finger device for interpretation. Simple motions, gestures, finger-writing, grip strength, and activation time, as well as more complex idioms consisting of multiple grips, are identified and quantified. We demonstrate the use of this technology as a human-computer interface, clinical feature generator, and means to characterize workplace tasks.

Subpopulation-specific patterns of intrinsic connectivity in mouse superficial dorsal horn as revealed by laser scanning photostimulation.

The primary goal of this study was to map the transverse distribution of local excitatory and inhibitory synaptic inputs to mouse lamina I spinal dorsal horn neurons, using laser scanning photostimulation. A sample of lamina II neurons was also studied for comparison. Lamina I neurons received excitatory synaptic input from both laminae I-II and the outer part of III-IV, especially the II/III border region, while the inhibitory input zones were mostly confined within I-II. The excitatory synaptic input zones showed a pronounced medial asymmetry, which was correlated with a matching asymmetry in the dendritic fields of the neurons. Inhibitory input from laminae III-IV was found in a subpopulation of neurons occupying a highly restricted zone, essentially one cell layer thick, immediately below the lamina I/II border, with morphological and physiological properties that were distinct from other laminar populations in the superficial dorsal horn, and that suggest a critical role in interlaminar communication. This subpopulation also received excitatory input from laminae III-IV. Within this subpopulation, inhibitory III-IV input was correlated with the presence of long ventral dendrites. Correlations between the distribution of synaptic input zones and dendritic fields support the concept that interlaminar communication is mediated in part via contacts made onto ventrally extending dendrites of superficial laminae neurons. The results point to the presence of cell type specificity in dorsal horn circuitry, and show how the study of connectivity can itself help identify previously unrecognized neuronal populations.