Wearable systems for measuring sweat rate and methods of using the same

Presented herein are systems and methods for measuring sweat rate of a subject using a wearable system. A sweat rate may be determined automatically based on one or more signals produced by a wetting sensor module in response to a presence of sweat in the wearable system. The one or more signals may be produced using a sweat presence monitoring device, for example comprising two or more electrodes that are operable to make conductance measurements. In some embodiments, sweat drops are periodically collected by the wearable system and individually detected by the wetting sensor module such that the sweat rate is determined based on the periodic detection of the drops. In some embodiments, an energy barrier is used to produce periodic flow of sweat through the wearable device detected by the wetting sensor module such that the sweat rate is determined based on the periodic flow.

Adaptive wake and sleep detection for wearable systems

Walter Karlen

Sleep problems and disorders have a serious impact on human health and wellbeing. The rising costs for treating sleep-related chronic diseases in industrialized countries demands efficient prevention. Low-cost, wearable sleep / wake detection systems which give feedback on the wearer's "sleep performance" are a promising approach to reduce the risk of developing serious sleep disorders and fatigue. Not all bio-medical signals that are useful for sleep / wake discrimination can be easily recorded with wearable systems. Sensors often need to be placed in an obtrusive location on the body or cannot be efficiently embedded into a wearable frame. Furthermore, wearable systems have limited computational and energetic resources, which restrict the choice of sensors and algorithms for online processing and classification. Since wearable systems are used outside the laboratory, the recorded signals tend to be corrupted with additional noise that influences the precision of classification algorithms. In this thesis we present the research on a wearable sleep / wake classifier system that relies on cardiorespiratory (ECG and respiratory effort) and activity recordings and that works autonomously with minimal user interaction. This research included the selection of optimal signals and sensors, the development of a custom-tailored hardware demonstrator with embedded classification algorithms, and the realization of experiments in real-world environments for the customization and validation of the system. The processing and classification of the signals were based on Fourier transformations and artificial neural networks that are efficiently implementable into digital signal controllers. Literature analysis and empiric measurements revealed that cardiorespiratory signals are more promising for a wearable sleep / wake classification than clinically used signals such as brain potentials. The experiments conducted during this thesis showed that inter-subject differences within the recorded physiological signals make it difficult to design a sleep / wake classification model that can generalize to a group of subjects. This problem was addressed in two ways: First by adding features from another signal to the classifier, that is, measuring the behavioral quiescence during sleep using accelerometers. Conducted research on different feature extraction methods from accelerometer data showed that this data generalizes well for distinct subjects in the study group. In addition, research on user-adaptation methods was conducted. Behavioral sleep and wake measures, notably the measurement of reactivity and activity, were developed to build up a priori knowledge that was used to adapt the classification algorithm automatically to new situations. This thesis demonstrates the design and development of a low-cost, wearable hardware and embedded software for on-line sleep / wake discrimination. The proposed automatic user-adaptive classifier is advantageous compared to previously suggested classification methods that generalize over multiple subjects, because it can take changes in the wearer's physiology and sleep / wake behavior into account without adjustment from a human expert. The results of this thesis contribute to the development of smart, wearable, bio-physiological monitoring systems which require a high degree of autonomy and have only low computational resources available. We believe that the proposed sleep / wake classification system is a first promising step toward a context-aware system for sleep management, sleep disorder prevention, and reduction of fatigue.

EPFL2009

Wearable and Self-Aware Machine Learning System for Online Cognitive Workload Monitoring and Drone Control

The use of robots in search and rescue is gaining particular interest, but singular skills are required to ensure efficient deployments in real missions. To face this problem, there is a need to develop more intuitive control interfaces. Moreover, to ensure high performance during high cognitive demanding tasks, such as in search and rescue missions, there is a need to combine both humanâs and robotâs skills. Respectively, humans and robots can adapt to new situations and optimize the execution of repetitive tasks. In this regard, novel share-control techniques have been developed to adapt the human-robot interaction, but to dynamically adapt this interaction, the information about the human state is missing. To address these problems, I first developed a novel wearable system that enhances the control of drones providing a more intuitive flying experience. As shown in chapter 2, this wearable system tracks the upper body movements and translates them into commands for a drone. The system has been tested with a simulator and demonstrated for the teleoperation of a real drone. Moreover, to ensure enduring operations, I proposed a method that drastically reduces communication, and consequently, improves energy efficiency by 11.9%. Second, in chapter 3, I presented a machine-learning approach for monitoring the cognitive workload level of a drone operator involved in search and rescue missions. My model combines the information of different features extracted from physiological signals, such as respiratory activity, electrocardiogram, photoplethysmogram, and skin temperature, acquired in a non-invasive way. To reduce both subject and day inter-variability of the signals, I explored different feature normalization techniques. Moreover, I adjusted the learning method for support vector machines to allow subject-specific optimizations. On a test set acquired from 34 volunteers, the proposed model distinguished between low and high cognitive workloads with an average accuracy of 87.3% and 91.2%, while controlling a drone simulator using both a traditional controller and the proposed FlyJacket design, respectively. Third, in chapter 4, I presented the integration of the method developed for cognitive workload monitoring, on a new single wearable embedded system, that also integrates the proposed drone controller design. On the hardware side, it includes a multi-channel physiological signals acquisition and a low-power processing platform that is suited for cognitive workload monitoring. On the software side, the proposed system includes novel energy-aware bio-signal processing and embedded machine learning methods. Moreover, to exploit the trade-offs between the required accuracy of the available energy of the system, I presented a new application of the concept of a scalable machine-learning method with different power-saving levels. Results showed that the proposed self-aware approach yields an increase of 78% of the battery lifetime without really affecting the classification accuracy. The proposed system, comprising a drone controller integrating a unit for cognitive workload monitoring, lays the foundations for the development of new-generation human-robot interfaces. With the information about the human state, we can close the loop of traditional share-control techniques, which will be able to dynamically adapt the level of interaction with semi-autonomous machines based on the need for the operator.

EPFL2020

Modular Design and Optimization of Biomedical Applications for Ultralow Power Heterogeneous Platforms

David Atienza Alonso, Luca Benini, Elisabetta De Giovanni, Benoît Walter Denkinger, Simone Machetti, Fabio Montagna, Miguel Peon Quiros

In the last years, remote health monitoring is becoming an essential branch of health care with the rapid development of wearable sensors technology. To meet the demand of new more complex applications and ensuring adequate battery lifetime, wearable sensors have evolved into multicore systems with advanced power-saving capabilities and additional heterogeneous components. In this article, we present an approach that applies optimization and parallelization techniques uncovered by modern ultralow power (ULP) platforms in the SW layers with the goal of improving the mapping and reducing the energy consumption of biomedical applications. Additionally, we investigate the benefit of integrating domain-specific accelerators to further reduce the energy consumption of the most computationally expensive kernels. Using 30-s excerpts of signals from two public databases, we apply the proposed optimization techniques on well-known modules of biomedical benchmarks from the state-of-the-art and two complete applications. We observe speed-ups of 5.17x and energy savings of 41.6% for the multicore implementation using a cluster of 8 cores with respect to single-core wearable sensor designs when processing a standard 12-lead electrocardiogram (ECG) signal analysis. Additionally, we conclude that the minimum workload required to take advantage of parallelization for a heartbeat classifier corresponds to the processing of 3-lead ECG signals, with a speed-up of 2.96x and energy savings of 19.3%. Moreover, we observe additional energy savings of up to 7.75% and 16.8% by applying power management and memory scaling to the multicore implementation of the 3-lead beat classifier and 12-lead ECG analysis, respectively. Finally, by integrating hardware (HW) acceleration we observe overall energy savings of up to 51.3% for the 12-lead ECG analysis.

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020