With the recent improvement of flexible electronics, wearable systems are becoming more and more unobtrusive and comfortable, pervading fitness and health-care applications. Wearable devices allow non-invasive monitoring of vital signs and physiological parameters, enabling advanced Human Machine Interaction (HMI) as well. On the other hand, battery lifetime remains a challenge especially when they are equipped with bio-medical sensors and not used as simple data logger. In this paper, we present a flexible wristband for EMG gesture recognition, designed on a flexible Printed Circuit Board (PCB) strip and powered by a small form-factor flexible solar energy panel. The proposed wristband executes a Support Vector Machine (SVM) algorithm reaching 94.02 % accuracy in recognition of 5 hand gestures. The system targets healthcare and HMI applications, and can be used to monitor patients during rehabilitation from stroke and neural traumas as well as to enable a simple gesture control interface (e.g. for smart-watches). Experimental results show the accuracy achieved by the algorithm and the lifetime of the device. By virtue of the low power consumption of the proposed solution and the on-board processing that limits the radio activity, the wristband achieves more than 500 hours with a single 200 mAh battery, and perpetual work with a small-form factor flexible solar panel.

Smart Wearable Wristband for EMG based Gesture Recognition Powered by Solar Energy Harvester

Drowsiness is one of the most prevalent causes of accidents in mining, driving and industrial activities carrying high personal risks and economic costs. For this reason, automatic detection of drowsiness is becoming an important application, and it is being integrated in a large variety of wearable and deeply embedded systems. Relevant effort has been spent in the past to quantify the drowsiness level from behavioral features exploiting eye tracking systems, dermal sensors or steering wheel movements. On the other hand, all these approaches lack of generality, they are highly intrusive and can only be applied in specific circumstances. A promising alternative approach is based on the extraction and processing of physiological features from the EEG using Brain Computer Interfaces (BCI). This work describes a wearable system capable of detecting drowsiness conditions and emitting alarms using only EEG signals, with three levels of alarm based on the blink duration and on the spectral power of alpha waves. This implementation aims to replace or complement the use of cameras and other sensors, extracting drowsiness information exploiting both behavioral and physiological features from EEG sensors only. The system was validated with 7 test subjects achieving detection accuracy of 85%, while being much more lightweight and compact than other state of the art methods.

/pdf/a-wearable-eeg-based-drowsiness-detection-system-with-blink-2onpspxgxp.pdf

A wearable EEG-based drowsiness detection system with blink duration and alpha waves analysis

Hybrid encoding algorithm for real time compressed electrocardiogram acquisition

Wireless sensor networks (WSNs) have greatly advanced in the past few decades and are now widely used, especially for remote monitoring; the list of potential uses seems endless. Three types of wireless sensor technologies (Bluetooth, ZigBee, and ANT) have been used to monitor the biomechanical and physiological activities of bicycles and cyclists, respectively. However, the wireless monitoring of these activities has faced some challenges. The aim of this paper is to highlight various methodologies for monitoring cycling to provide an effective and efficient way to overcome the various challenges and limitations of sports cycling using wireless sensor interfaces. Several design criteria were reviewed and compared with different solutions for the implementation of current WSN research, such as low power consumption, long distance communications, small size, and light weight. Conclusions were drawn after observing the example of an advanced and adaptive network technology (ANT) network highlighting reduced power consumption and prolonged battery life. The power saving achieved in the slave node was 88–95% compared to the similar ANT protocol used in the medical rehabilitation.

/pdf/a-survey-on-energy-efficient-wireless-sensor-networks-for-4biy2oi59i.pdf

A Survey on Energy Efficient Wireless Sensor Networks for Bicycle Performance Monitoring Application

A wearable wireless general purpose bio-signal acquisition prototype system is presented in this paper. Three types of bio-signals could be acquired by the system then wirelessly transmitted to the computer for real-time processing. The prototype experimental results show that the system could acquire electrocardiogram (ECG), surface-electromyography (s-EMG) and electroencephalogram (EEG) for home healthcare and telemedicine.

A Wearable Wireless General Purpose Bio-signal Acquisition Prototype System for Home Healthcare

12-Lead ECG Data Acquisition System Based on ADS1298

MCU-Controlling Based Bluetooth Data Transferring System

Abstract In this paper, the vibration model of the piezoelectric cantilever, the piezoelectric and electromagnetic output model are established. The simulation analysis of the cantilever vibration is performed using Comsol Multiphysics software. The effects of structural parameters, including cantilever length, cantilever thickness, piezoelectric layer length, piezoelectric layer thickness, coil size and position, on the resonance frequency and output voltage were analyzed. A cantilever vibration energy collection device is designed. The feasibility and accuracy of theoretical analysis were verified through the experiment, and the resonance frequency and the output voltage of the experiment basically agree with the theoretical calculation value.

Environmental vibration energy collection technology based on piezoelectric and electromagnetic model

Road safety for automated vehicles requires accurate and early detection of stationary objects in the vehicle’s path. Radar can use doppler to effectively identify stationary objects and make these identifications at long range and in severe weather and poor light conditions. In this paper, we propose a radar-based stationary object detection system that combines signal processing techniques with machine learning technology to detect stationary in-path objects from the low level spectra of front looking radars. The proposed system consists of novel signal and image processing methods to extract key features from the raw data, which are fed into a long short-term memory (LSTM) to determine the probability of a stationary object in-lane at each range. Experiments with collected data in controlled and uncontrolled scenarios demonstrate the effectiveness of our approach. 

Yihang Zhang

Papers

12-Lead ECG Data Acquisition System Based on ADS1298

MCU-Controlling Based Bluetooth Data Transferring System

Environmental vibration energy collection technology based on piezoelectric and electromagnetic model

Machine Learning Based Early Debris Detection Using Automotive Low Level Radar Data