Hippo

Abstract

Hand-grip strength is widely used to estimate muscle strength and it serves as a general indicator of the overall health of a person, particularly in aging adults. Hand-grip strength is typically estimated using dynamometers or specialized force resistant pressure sensors embedded onto objects. Both of these solutions require the user to interact with a dedicated measurement device which unnecessarily restricts the contexts where estimates are acquired. We contribute HIPPO, a novel non-intrusive and opportunistic method for estimating hand-grip strength from everyday interactions with objects. HIPPO re-purposes light sensors available in wearables (e.g., rings or gloves) to capture changes in light reflectivity when people interact with objects. This allows HIPPO to non-intrusively piggyback everyday interactions for health information without affecting the user's everyday routines. We present two prototypes integrating HIPPO, an early smart glove proof-of-concept, and a further optimized solution that uses sensors integrated onto a ring. We validate HIPPO through extensive experiments and compare HIPPO against three baselines, including a clinical dynamometer. Our results show that HIPPO operates robustly across a wide range of everyday objects, and participants. The force strength estimates correlate with estimates produced by pressure-based devices, and can also determine the correct hand grip strength category with up to 86% accuracy. Our findings also suggest that users prefer our approach to existing solutions as HIPPO blends the estimation with everyday interactions.

Contact Resistance Sensing for Touch and Squeeze Interactions

“…There are a number of studies on squeezable interfaces in which rigid sensors are embedded. Some typical sensing technologies are optical sensing [13,14,21,24,34,35], air pressure sensing [44,45], force sensing [2,5,17,19,32,33] and acoustic sensing [23,26]. However, if a completely soft experience is expected, hard components could distract from the experience of softness when squeezing.…”

“…Some systems utilized commercially available materials to build squeeze sensors. Some of them are embedded with rigid sensors to detect compression [2,5,13,14,17,19,21,23,24,26,[32][33][34][35]. By contrast, to remove the rigid components from the compression part of the sensor, some studies utilized commercially available soft conductive materials to detect the deformation of the soft interface.…”

Multi-domain Estimation of Health-Related Physical Fitness from Smartwatches

“…However, measuring and tracking the fitness levels for all HRPF domains requires specialized laboratory equipment and personnel, which are expensive and less applicable for general population. Wearable technology mitigates this issue and has proven to be a reliable alternative capable of providing useful estimates of some HRPF domains [4][5][6][7] . Previous work has predicted the HRPF domains from anthropometric measures and laboratory bioelectrical impedance analysis data (BIA) under the supervision of health professionals 8,9 .…”