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

Healthcare Wearable Sensors Adhesion to Human Fingernails and Toenails

A novel adhesion method of a sensor to a fingernail is described. Wearable sensors can provide health insights to humans for a wide variety of benefits, such as continuous wellness monitoring and disease monitoring throughout a patient's daily life. While there are many locations to place these wearable sensors on the body, we will focus on the fingertip, one significant way that people interact with the world. Like artificial fingernails used for aesthetics, wearable healthcare sensors can be attached to the fingernail for short or long time periods with minimal irritation and disruption to daily life. In this study the structure and methods of healthcare sensors' attachment and removal have been explored to support (1) the sensor functional requirements, (2) biological and environmentally compatible solutions and (3) ease of attachment and removal for short- and long-term user applications. Initial fingernail sensors were attached using a thin adhesive layer of commonly available cosmetic nail glue. While this approach allowed for easy application and strong adhesion to the nail, the removal could expose the fingernail and finger to a commercially available cosmetic nail removal (acetone-based chemical) for extended times measured in minutes. Therefore, a novel structure and method were developed for rapid healthcare sensor attachment and removal in seconds, which supported both the sensor functional objectives and the biologically and environmentally safe use objectives.

Fractional modeling of flexural behavior of toenail plates: First step for clinical purposes

This work presents an identification procedure of flexural behavior of toenail plates in twenty subjects with no history of feet or nail injury as of in-vivo measurements. In particular, four different mechanical models are considered to describe such properties, ranging from the pure elastic to viscoelastic behavior, the latter from the classical and fractional points of view. The quality of the adjustment of each model is examined by a group of performance indices. Experimental data show that the best identification is achieved by the fractional order viscoelastic model for all subjects. These novel results in modeling flexural behavior of toenails are consistent with the published literature suggesting that viscoelastic materials may be successfully modeled with derivatives of fractional order. This could contribute, together with additional variables, to help health professionals, and more especially podiatrists, to have reliable and quantitative measures of the nail flexural behavior which can be susceptible of treatment or for prevention.

Turning the Finger into a Writing Tool

A novel writing platform composed of a wearable sensor on the fingernail and classification algorithms is described. Findings from using this platform to translate fingertip writing into shapes, letters, and numbers on a range of surfaces are reported. The new wearable platform leverages an architecture with miniaturized electronic circuitry to precisely measure a set of forces in the longitudinal and transverse directions using multiple strain gauges. We find that the directional pressure patterns are translated from the fingertip to the fingernail. Deformation of fingernails in the longitudinal and transverse directions are detected by the fingernail sensor which sends the data wirelessly to a portable electronic system. Fingernail pressure patterns are categorized through signal processing to recognize a range of shapes, numbers, and letters, enabling fingertip writing recognition. Use of the writing platform following a short training session, shows human fingertip writing on multiple surfaces were automatically transcribed to a computer.