While the incidence of sleep disorders is continuously increasing in western societies, there is a clear demand for technologies to asses sleep-related parameters in ambulatory scenarios. The present study introduces a novel concept of accurate sensor to measure RR intervals via the analysis of photo-plethysmographic signals recorded at the wrist. In a cohort of 26 subjects undergoing full night polysomnography, the wrist device provided RR interval estimates in agreement with RR intervals as measured from standard electrocardiographic time series. The study showed an overall agreement between both approaches of 0.05 ± 18 ms. The novel wrist sensor opens the door towards a new generation of comfortable and easy-to-use sleep monitors.
In this paper we present a personal, cost-effective, multi-parametric monitoring system based on textile platforms and portable sensing devices for the long term and short term acquisition of data from bipolar patients affected by mood disorders. The system allows the early indication and prevention of bipolar state relapse situations. The bipolar mood state of the patients is etimated from several physiogical and physical cues such as biochemical markers, voice analysis and a behavioural index correlated to patient state.
In this paper, we present the evaluation of a new smart shoe capable of performing gait analysis in real time. The system is exclusively based on accelerometers which minimizes the power consumption. The estimated parameters are activity class (rest/walk/run), step cadence, ground contact time, foot impact (zone, strength, and balance), forward distance, and speed. The different parameters have been validated with a customized database of 26 subjects on a treadmill and video data labeled manually. Key measures for running analysis such as the cadence is retrieved with a maximum error of 2%, and the ground contact time with an average error of 3.25%. The classification of the foot impact zone achieves a precision between 72% and 91% depending of the running style. The presented algorithm has been licensed to ICON Health & Fitness Inc. for their line of wearables under the brand iFit.
This article presents the performance results of a novel algorithm for swimming analysis in real-time within a low-power wrist-worn device. The estimated parameters are: lap count, stroke count, time in lap, total swimming time, pace/speed per lap, total swam distance, and swimming efficiency (SWOLF). In addition, several swimming styles are automatically detected. Results were obtained using a database composed of 13 different swimmers spanning 646 laps and 858.78 min of total swam time. The final precision achieved in lap detection ranges between 99.7% and 100%, and the classification of the different swimming styles reached a sensitivity and specificity above 98%. We demonstrate that a swimmers performance can be fully analyzed with the smart bracelet containing the novel algorithm. The presented algorithm has been licensed to ICON Health & Fitness Inc. for their line of wearables under the brand iFit.
In this paper, we present the evaluation of a new physical activity profiling system embedded in a wrist-located device. We propose a step counting and an energy expenditure (EE) method, and evaluate their accuracy against gold standard references. To this end, we used an actimetry sensor on the waist and an indirect calorimetry monitoring device on a population of 13 subjects to obtain step count and metabolic equivalent task (kcal/kg/h) referenced values. The subjects followed a protocol that spanned a given set of activities (lying, standing, walking, running) at a wide range of intensities. The performance of the EE model was characterized by a root-mean-square error (RMSE) of 1.22±0.34kcal/min, and step-count model at regular walking/running speeds by 0.71±0.06step/10sec.
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