Ultra-Low Power Wearable Infant Sleep Position Sensor.

Numerous wearable sensors have been developed for a variety of needs in medical/healthcare/wellness/sports applications, but there are still doubts about their usefulness due to uncomfortable fit or frequent battery charging. Because the size or capacity of battery is the major factor affecting the convenience of wearable sensors, power consumption must be reduced. We developed a method that can significantly reduce the power consumption by introducing a signal repeater and a special switch that provides power only when needed. Antenna radiation characteristics are an important factor in wireless wearable sensors, but soft material encapsulation for comfortable fit results in poor wireless performance. We improved the antenna radiation characteristics by a local encapsulation patterning. In particular, ultra-low power operation enables the use of paper battery to achieve a very thin and flexible form factor. Also, we verified the human body safety through specific absorption rate simulations. With these methods, we demonstrated a wearable infant sleep position sensor. Infants are unable to call for help in unsafe situations, and it is not easy for caregivers to observe them all the time. Our wearable sensor detects infants' sleep positions in real time and automatically alerts the caregivers when needed.

Ultra-Low-Power Wearable Vibration Sensor with Highly Accurate Embedded Classifier.

Reducing the power consumption of wearable sensors is a very important issue in relation to the device usage time and form factor. However, continuous wireless communication to analyze the measured signal in real-time significantly increases the power consumption of the wearable sensor. In this study, we propose a wearable vibration sensor that operates with extremely low power through an embedded signal classifier, which exhibits high accuracy and low calculation load. We demonstrate cough detection through the proposed sensor system. The result exhibits an accuracy of 93.0%, which is 24.3% higher than the conventional embedded classification algorithm. Also, the proposed approach reduces the average power consumption of the wearable sensor by 8.8 times. Clinical Relevance-People can measure the vibration from the body using an ultra-low-power wearable sensor. It provides a solution to automatically monitor cough symptoms in numerous patients.

Study on Optimal Position and Covering Pressure of Wearable Neck Microphone for Continuous Voice Monitoring.

Vocal cord disorder is one of the important health problems, especially in noisy industrial sites where excessive voice is required. A convenient and reliable communication method is required in a noisy environment to prevent the related disorders. However, the signal sensitivity of previous neck microphones is still insufficient to accurately convey the voice. In this study, we developed a skin-attachable neck microphone with a lightweight and flexible form factor. Also, we optimized the attachment position and covering pressure to maximize the signal sensitivity. As a result, we obtained the optimal position near the thyroid cartilage and confirmed that the signal sensitivity is the highest when the covering pressure is approximately 4 mmHg.Clinical Relevance- People can measure the voice status using a wearable neck microphone at the optimal position and covering pressure. It provides a solution to keep the vocal cords in good health even in a noisy environment.

A Wearable System for Heart Rate Recovery Evaluation with Real-Time Classification on Exercise Condition.

Heart rate recovery (HRR) is a convenient index to assess a cardiovascular autonomic function response to physical exercise. HRR monitoring during daily exercise can be an effective way to verify cardiorespiratory performance. Because HRR varies depending on exercise intensity and resting condition, an exercise condition needs to be acquired for a reliable HRR analysis. This study presents a wearable system for HRR evaluation with automatic labeling of exercise conditions using real-time activity classification. We developed an activity classification algorithm using two features from accelerometer sensor: an acceleration peak and an angle tilt peak. The classification algorithm was applied to a chest-attached wearable device with an embedded electrocardiogram sensor and accelerometer sensors. We classified daily activities such as running, walking, and postural transitions performed under supervised conditions. The wearable device system accurately detected activities with a sensitivity of 99.2 % and posture transitions with a sensitivity of 92 % and specificity of 93.3 % for seven healthy subjects. The proposed wearable system can help monitor HRR during exercise training by labeling the exercise condition simultaneously.

Quantitative Muscle Fatigue Estimation with High SNR Flexible Skin Electrode.

In recent years, surface electromyography (sEMG) has been commonly used to diagnose neuromuscular abnormalities. Since sEMG measures electrical signals from various tangled muscle nerves, a high signal-to-noise ratio (SNR) is required to estimate the condition accurately. Previously, Ag/AgCl electrodes were widely used for sEMG measurements, but noble metals are more advantageous for long-term and continuous measurement. In this study, we improved the SNR of bioelectrical signals by increasing the surface area of a flexible skin-electrode made of noble metal. The electrode surface area was increased by 1.38 times with electroplating, and the SNR of sEMG was improved by 1.63 times. Utilizing the sEMG signals with high SNR, we propose a new muscle fatigue estimation algorithm for monitoring the muscle condition in real-time.