Designing a Hybrid Energy-Efficient Harvesting System for Head- or Wrist-Worn Healthcare Wearable Devices.

Battery power is crucial for wearable devices as it ensures continuous operation, which is critical for real-time health monitoring and emergency alerts. One solution for long-lasting monitoring is energy harvesting systems. Ensuring a consistent energy supply from variable sources for reliable device performance is a major challenge. Additionally, integrating energy harvesting components without compromising the wearability, comfort, and esthetic design of healthcare devices presents a significant bottleneck. Here, we show that with a meticulous design using small and highly efficient photovoltaic (PV) panels, compact thermoelectric (TEG) modules, and two ultra-low-power BQ25504 DC-DC boost converters, the battery life can increase from 9.31 h to over 18 h. The parallel connection of boost converters at two points of the output allows both energy sources to individually achieve maximum power point tracking (MPPT) during battery charging. We found that under specific conditions such as facing the sun for more than two hours, the device became self-powered. Our results demonstrate the long-term and stable performance of the sensor node with an efficiency of 96%. Given the high-power density of solar cells outdoors, a combination of PV and TEG energy can harvest energy quickly and sufficiently from sunlight and body heat. The small form factor of the harvesting system and the environmental conditions of particular occupations such as the oil and gas industry make it suitable for health monitoring wearables worn on the head, face, or wrist region, targeting outdoor workers.

DELMEP: a deep learning algorithm for automated annotation of motor evoked potential latencies.

The analysis of motor evoked potentials (MEPs) generated by transcranial magnetic stimulation (TMS) is crucial in research and clinical medical practice. MEPs are characterized by their latency and the treatment of a single patient may require the characterization of thousands of MEPs. Given the difficulty of developing reliable and accurate algorithms, currently the assessment of MEPs is performed with visual inspection and manual annotation by a medical expert; making it a time-consuming, inaccurate, and error-prone process. In this study, we developed DELMEP, a deep learning-based algorithm to automate the estimation of MEP latency. Our algorithm resulted in a mean absolute error of about 0.5 ms and an accuracy that was practically independent of the MEP amplitude. The low computational cost of the DELMEP algorithm allows employing it in on-the-fly characterization of MEPs for brain-state-dependent and closed-loop brain stimulation protocols. Moreover, its learning ability makes it a particularly promising option for artificial-intelligence-based personalized clinical applications.

Efficient Respiratory Rate Extraction on a Smartwatch.

Using the Photoplethysmogram (PPG) sensor of a smartwatch to extract Respiratory Rate (RR) is very attractive. However, existing algorithms suffer from lack of accuracy and susceptibility to noise and movement artifacts. To tackle this issue, we propose performing Frequency Domain Peak (FDP) analysis using the Frequency Modulation (FM) feature. Moreover, our analysis of existing methods show that in contrast to the common practice Smart Fusion (SFU), despite incurring extra computational costs, is very little helpful. It is hence more preferable and efficient to avoid SFU. The proposed method shows an improvement of at least 130% in the Figure of Merit (FoM) and has more than 60% smaller mean error. Therefore, it can be reliably used in a wide range of applications.

A simple algorithm for emotion recognition, using physiological signals of a smart watch.

Recently, it has become easier and more common to measure physiological signals through wearable devices such as smart watches. Extracting emotional states of individuals with problems expressing it, such as autistic individuals, can help their parents, friends, and therapists to obtain a better understanding of what they feel throughout their day. Although emotion recognition methods based on physiological signals have been studied for many years, there is a smaller body of literature about systems working with data obtained from wearable devices. In this paper, we present an emotion recognition system with a small footprint suitable for limited resources of wearable devices. Other than identifying the emotions (with a success rate of 65%), The proposed system also tags each recognition with a confidence value (on average 57%).