A Batteryless Motion-Adaptive Heartbeat Detection System-on-Chip Powered by Human Body Heat

Abstract

This paper presents a batteryless heartbeat detection system-on-chip (SoC) powered by human body heat. An adaptive threshold generation architecture using pulse-width locked loop (PWLL) is developed to detect heartbeats from electrocardiogram (ECG) in the presence of motion artifacts. The sensing system is autonomously powered by harvesting thermal energy from human body heat using a thermoelectric generator (TEG) coupled to a low-voltage, self-starting boost converter and integrated power management system. The SoC was implemented in a 0.18 μm CMOS process and is fully functional with a minimum input power of 20 μW, provided by a portable TEG at 20 mV with a ~0.5 °C temperature gradient. The complete system demonstrates motion-adaptive, power-autonomous heartbeat detection for sustainable healthcare using wearable devices.

Research on Real-Time Detection of Sprint Error Based on Visual Features and Internet of Things

“…On the basis of the three-dimensional visual analysis of sprint fault action based on computer vision features and RFID group tags, through the feature analysis, it is necessary to carry out three-dimensional visual detection of sprint fault action. In order to increase the image reconstruction level of sprint movement, it is necessary to select the appropriate corner matching method to obtain the distribution parameters of each modeling point and to identify the wrong movement of sprint [15]. To this end, this paper proposes a three-dimensional vision detection method for sprint errors based on edge detection in the contour wave domain.…”

“…In formula (15), I x is the error feature of the sprint motion image, I y is the vertical zoom amount in the motion, and I x I y is the correlation between the wrong action and the correct action. We carry out the first-order Taylor expansion on the formula (15) and use the formula ( 16) to obtain the feature quantity of edge detection in the contour wave domain:…”

Thermally Chargeable Supercapacitor with 3D Ti3C2Tx MXene Hollow Sphere Based Freestanding Electrodes

“…[ 2,3 ] The harvest of thermal energy generated in the human body when subjected to a temperature gradient (Δ T ) shows unparalleled advantages of continuation, spontaneity, safety, and wide adaptability compared to the mechanical energy coming from human motion, walking, and so on. [ 4–6 ]…”

“…adaptability compared to the mechanical energy coming from human motion, walking, and so on. [4][5][6] With the goal to achieve efficient body heat harvesting, various thermoelectric devices composed of thermal thermoosmotic energy conversation system, and thermoelectric generators (TE), [7][8][9] thermally chargeable supercapacitor (TCSC) [10][11][12][13][14] with high output power, [8,15] lightweight, [16,17] small size, [8,18,19] low cost, [19] and flexibility [20,21] have been explored for transferring thermal energy into electricity primarily through the use of the Seebeck effect. [22][23][24] TCSC devices requiring no special charge circle or equipment compensate for the lack of the energy storage harvested by the TE, accomplishing the fabrication of selfpowered devices.…”

Self-Powered Point-of-Care Device for Galvanic Cell-Based Sample Concentration Measurement

“…A final shirt with a hidden integrated TEH was presented, demonstrating the power generated during different real-life activities, generating a power between 0.5 mW and 5 mW at ambient temperatures from 15°C to 27°C. In [11], a proof-of-concept thermoelectric system could generate up to 20.3 µW cm −2 with a gradient temperature of 12°C and power the microcontroller of a wearable device, and in [12], a battery-less heartbeat detection systemon-a-chip (SoC) was powered by a thermoelectric generator with a temperature gradient of 0.5°C and a minimum input power of 20 µW.…”