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Early detection of cognitive decline is essential to study mild cognitive impairment and Alzheimer's Disease in order to develop targeted interventions and prevent or stop the progression of dementia. This requires continuous and longitudinal assessment and tracking of the related physiological and behavioral changes during daily life. In this paper, we present a low cost and low power wearable system custom designed to track the trends in speech, gait, and cognitive stress while also considering the important human factor needs such as privacy and compliance. In the form factors of a wristband and waist-patch, this multimodal, multi-sensor system measures inertial signals, sound, heart rate, electrodermal activity and pulse transit time. A total power consumption of 2.6 mW without any duty cycling allows for more than 3 weeks of run time between charges when 1500 mAh batteries are used.Clinical Relevance- Much earlier detection of Alzheimer's disease and related dementias may be possible by continuous monitoring of physiological and behavioral state using application specific wearable sensors during the activities of daily life.

Wearable and wireless monitoring of biomarkers such as lactate in sweat can provide a deeper understanding of a subject's metabolic stressors, cardiovascular health, and physiological response to exercise. However, the state-of-the-art wearable and wireless electrochemical systems rely on active sweat released either via high-exertion exercise, electrical stimulation (such as iontophoresis requiring electrical power), or chemical stimulation (such as by delivering pilocarpine or carbachol inside skin), to extract sweat under low-perspiring conditions such as at rest. Here, we present a continuous sweat lactate monitoring platform combining a hydrogel for osmotic sweat extraction, with a paper microfluidic channel for facilitating sweat transport and management, a screen-printed electrochemical lactate sensor, and a custom-built wireless wearable potentiostat system. Osmosis enables zero-electrical power sweat extraction at rest, while continuous evaporation at the end of a paper channel allows long-term sensing from fresh sweat. The positioning of the lactate sensors provides near-instantaneous sensing at low sweat volume, and the custom-designed potentiostat supports continuous monitoring with ultra-low power consumption. For a proof of concept, the prototype system was evaluated for continuous measurement of sweat lactate across a range of physiological activities with changing lactate concentrations and sweat rates: for 2 h at the resting state, 1 h during medium-intensity exercise, and 30 min during high-intensity exercise. Overall, this wearable system holds the potential of providing comprehensive and long-term continuous analysis of sweat lactate trends in the human body during rest and under exercising conditions.

Cough detection can provide an important marker to monitor chronic respiratory conditions. However, manual techniques which require human expertise to count coughs are both expensive and time-consuming. Recent Automatic Cough Detection Algorithms (ACDAs) have shown promise to meet clinical monitoring requirements, but only in recent years they have made their way to non-clinical settings due to the required portability of sensing technologies and the extended duration of data recording. More precisely, these ACDAs operate at high sampling frequencies, which leads to high power consumption and computing requirements, making these difficult to implement on a wearable device. Additionally, reproducibility of their performance is essential. Unfortunately, as the majority of ACDAs were developed using private clinical data, it is difficult to reproduce their results. We, hereby, present an ACDA that meets clinical monitoring requirements and reliably operates at a low sampling frequency. This ACDA is implemented using a convolutional neural network (CNN), and publicly available data. It achieves a sensitivity of 92.7%, a specificity of 92.3%, and an accuracy of 92.5% using a sampling frequency of just 750 Hz. We also show that a low sampling frequency allows us to preserve patients' privacy by obfuscating their speech, and we analyze the trade-off between speech obfuscation for privacy and cough detection accuracy.Clinical relevance-This paper presents a new cough detection technique and preliminary analysis on the trade-off between detection accuracy and obfuscation of speech for privacy. These findings indicate that, using a publicly available dataset, we can sample signals at 750 Hz while still maintaining a sensitivity above 90%, suggested to be sufficient for clinical monitoring [1].

Continuous, non-invasive wearable measurement of metabolic biomarkers could provide vital insight into patient condition for personalized health and wellness monitoring. We present our efforts towards developing a wearable solar-powered electrochemical platform for multimodal sweat based metabolic monitoring. This wrist-worn wearable system consists of a flexible photovoltaic cell connected to a circuit board containing ultra low power circuitry for sensor data collection, energy harvesting, and wireless data transmission, all integrated into an elastic fabric wristband. The system continuously samples amperometric, potentiometric, temperature, and motion data and wirelessly transmits these to a data aggregator. The full wearable system is 7.5 cm long and 5 cm in diameter, weighs 22 grams, and can run directly from harvested light energy. Relatively low levels of light such as residential lighting (∼200 lux) are sufficient for continuous operation of the system. Excess harvested energy is stored in a small 37 mWh lithium polymer battery. The battery can be charged in ∼14 minutes under full sunlight and can power the system for ∼8 days when fully charged. The system has an average power consumption of 176 µW. The solar-harvesting performance of the system was characterized in a variety of lighting conditions, and the amperometric and potentiometric electrochemical capabilities of the system were validated in vitro.Clinical relevance-The presented solar-powered wearable system enables continuous wireless multi-modal electrochemical monitoring for uninterrupted sensing of metabolic biomarkers in sweat while harvesting energy from indoor lighting or sunlight.

Operating at low sweat rates, such as those experienced by humans at rest, is still an unmet need for state-of-the-art wearable sweat harvesting and testing devices for lactate. Here, we report the on-skin performance of a non-invasive wearable sweat sampling patch that can harvest sweat at rest, during exercise, and post-exercise. The patch simultaneously uses osmosis and evaporation for long-term (several hours) sampling of sweat. Osmotic sweat withdrawal is achieved by skin-interfacing a hydrogel containing a concentrated solute. The gel interfaces with a paper strip that transports the fluid via wicking and evaporation. Proof of concept results show that the patch was able to sample sweat during resting and post-exercise conditions, where the lactate concentration was successfully quantified. The patch detected the increase in sweat lactate levels during medium level exercise. Blood lactate remained invariant with exercise as expected. We also developed a continuous sensing version of the patch by including enzymatic electrochemical sensors. Such a battery-free, passive, wearable sweat sampling patch can potentially provide useful information about the human metabolic activity.

Comprehensive metabolic panels are the most reliable and common methods for monitoring general physiology in clinical healthcare. Translation of this clinical practice to personal health and wellness tracking requires reliable, non-invasive, miniaturized, ambulatory, and inexpensive systems for continuous measurement of biochemical analytes. We report the design and characterization of a wearable system with a flexible sensor array for non-invasive and continuous monitoring of human biochemistry. The system includes signal conditioning, processing, and transmission parts for continuous measurement of glucose, lactate, pH, and temperature. The system can operate three discrete electrochemical cells. The system draws 15 mA under continuous operation when powered by a 3.7 V 150 mAh battery. The analog front-end of the electrochemical cells has four potentiostats and three multiplexers for multiplexed and parallel readout from twelve working electrodes. Utilization of redundant working electrodes improves the measurement accuracy of sensors by averaging chronoamperometric responses across the array. The operation of the system is demonstrated in vitro by simultaneous measurement of glucose and lactate, pH, and skin temperature. In benchtop measurements, the sensors are shown to have sensitivities of 26.31 μA mM -1 ·cm -2 for glucose, 1.49 μA mM -1 ·cm -2 for lactate, 54 mV·pH -1 for pH, and 0.002 °C -1 for temperature. With the custom wearable system, these values were 0.84 ± 0.03 mV μM -1 ·cm -2 or glucose, 31.87 ± 9.03 mV mM -1 ·cm -2 for lactate, 57.18 ± 1.43 mV·pH -1 for pH, and 63.4 μV·°C -1 for temperature. This miniaturized wearable system enables future evaluation of temporal changes of the sweat biomarkers., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Wearable health monitoring has emerged as a promising solution to the growing need for remote health assessment and growing demand for personalized preventative care and wellness management. Vital signs can be monitored and alerts can be made when anomalies are detected, potentially improving patient outcomes. One major challenge for the use of wearable health devices is their energy efficiency and battery-lifetime, which motivates the recent efforts towards the development of self-powered wearable devices. This article proposes a method for context aware dynamic sensor selection for power optimized physiological prediction using multi-modal wearable data streams. We first cluster the data by physical activity using the accelerometer data, and then fit a group lasso model to each activity cluster. We find the optimal reduced set of groups of sensor features, in turn reducing power usage by duty cycling these and optimizing prediction accuracy. We show that using activity state-based contextual information increases accuracy while decreasing power usage. We also show that the reduced feature set can be used in other regression models increasing accuracy and decreasing energy burden. We demonstrate the potential reduction in power usage using a custom-designed multi-modal wearable system prototype.

A wearable skin hydration sensor in the form of a capacitor is demonstrated based on skin impedance measurement. The capacitor consists of two interdigitated or parallel electrodes that are made of silver nanowires (AgNWs) in a polydimethylsiloxane (PDMS) matrix. The flexible and stretchable nature of the AgNW/PDMS electrode allows conformal contact to the skin. The hydration sensor is insensitive to the external humidity change and is calibrated against a commercial skin hydration system on an artificial skin over a wide hydration range. The hydration sensor is packaged into a flexible wristband, together with a network analyzer chip, a button cell battery, and an ultralow power microprocessor with Bluetooth. In addition, a chest patch consisting of a strain sensor, three electrocardiography electrodes, and a skin hydration sensor is developed for multimodal sensing. The wearable wristband and chest patch may be used for low-cost, wireless, and continuous monitoring of skin hydration and other health parameters., (© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)