Automatic car setting adjustments by identifying driver with health watch wearable or in-car sensors

In one embodiment, a wearable device comprising one or more sensors is presented that receives one or more physiological parameters of a user sensed by the one or more sensors and causes adjustment of one or more vehicle parameters based on the one or more physiological parameters

Apparatus for measuring a physiological parameter using a wearable sensor

A wearable physiological sensor has a housing and a gas-permeable support structure carried by the housing, which contacts the skin of the subject. An air space is provided between the support structure and the housing. Movement of the support structure relative to the housing is sensed. This provides a sensor which is comfortable for the subject and provides good sensitivity in that motion being detected (e.g. an arterial pulse) only needs to impart kinetic energy to the support structure, with a relatively low inertia

Simulated case management of home telemonitoring to assess the impact of different alert algorithms on work-load and clinical decisions

© 2017 The Author(s). Background: Home telemonitoring (HTM) of chronic heart failure (HF) promises to improve care by timely indications when a patient's condition is worsening. Simple rules of sudden weight change have been demonstrated to generate many alerts with poor sensitivity. Trend alert algorithms and bio-impedance (a more sensitive marker of fluid change), should produce fewer false alerts and reduce workload. However, comparisons between such approaches on the decisions made and the time spent reviewing alerts has not been studied. Methods: Using HTM data from an observational trial of 91 HF patients, a simulated telemonitoring station was created and used to present virtual caseloads to clinicians experienced with HF HTM systems. Clinicians were randomised to either a simple (i.e. an increase of 2 kg in the past 3 days) or advanced alert method (either a moving average weight algorithm or bio-impedance cumulative sum algorithm). Results: In total 16 clinicians reviewed the caseloads, 8 randomised to a simple alert method and 8 to the advanced alert methods. Total time to review the caseloads was lower in the advanced arms than the simple arm (80 ± 42 vs. 149 ± 82 min) but agreements on actions between clinicians were low (Fleiss kappa 0.33 and 0.31) and despite having high sensitivity many alerts in the bio-impedance arm were not considered to need further action. Conclusion: Advanced alerting algorithms with higher specificity are likely to reduce the time spent by clinicians and increase the percentage of time spent on changes rated as most meaningful. Work is needed to present bio-impedance alerts in a manner which is intuitive for clinicians