Progress in Data Acquisition of Wearable Sensors

Abstract

Wearable sensors have demonstrated wide applications from medical treatment, health monitoring to real-time tracking, human-machine interface, smart home, and motion capture because of the capability of in situ and online monitoring. Data acquisition is extremely important for wearable sensors, including modules of probes, signal conditioning, and analog-to-digital conversion. However, signal conditioning, analog-to-digital conversion, and data transmission have received less attention than probes, especially flexible sensing materials, in research on wearable sensors. Here, as a supplement, this paper systematically reviews the recent progress of characteristics, applications, and optimizations of transistor amplifiers and typical filters in signal conditioning, and mainstream analog-to-digital conversion strategies. Moreover, possible research directions on the data acquisition of wearable sensors are discussed at the end of the paper.

Soft Sensors and Actuators for Wearable Human–Machine Interfaces

“…124 Furthermore, for HHMI sensors to be effective, they must be capable of accurately detecting tactile signals without interference from thermal and mechanical noises that result from changes in temperature and vibration when the device is in contact with the skin, as well as environmental noises such as electromagnetic interference (EMI), 125 radio frequency interference (RFI), 126 or acoustic noise. Although signal processing algorithms have been used to extract signals from noise and minimize signal distortion through conditioning methods such as amplification, low/high pass filters, calibration, and compensation for the signal, 127 it is important to note that accurate recognition of user input depends on a rational design of sensor itself. 128,129 Therefore, careful consideration and design of the sensor is crucial to ensure accurate signal recognition, even before employing signal processing techniques.…”

Wearable Sensor Data Classification for Identifying Missing Transmission Sequence Using Tree Learning

“…The encoder classifies the actual types and classes of healthcare data. Moreover, the works in [ 8 , 30 ] suggests the continuous transmissions. The proposed model minimizes the latency ratio in the computation process.…”

“…The data transmission technique creates an effective communication service among the devices. A priority-based wearable sensor data transmission method is used for the patient’s diagnosis process [ 7 , 8 ]. A feature extraction approach is implemented in the method which extracts the important features and patterns for sensor data.…”

Wearable sensing electronic devices with health monitoring function

“…In the wearable sensing system, the drain-source interface can be used as a biometric unit. The charge transfer caused by the change of analyte concentration will cause the change of gate voltage, and these differences will cause the change of leakage current, and then convert and amplify the chemical signal into an electrical signal [2]. This part of the power consumption optimization focuses on optimizing the transistor in the signal regulation equipment.…”

“…The sensor then converts the response of the receptors into useful signals that the user can use to take effective action. Such smart devices are expected to be applied in clinical and other areas of modern medicine [2]. But there are still some key problems with smart devices.…”