Human Body Heat Based Thermoelectric Harvester with Ultra-Low Input Power Management System for Wireless Sensors Powering
Bibliographic record
Abstract
Energy harvesting (EH) technique has been proposed as a favorable solution for addressing the power supply exhaustion in a wireless sensor node and prolong the operating time for a wireless sensor network. Thermoelectric energy generator (TEG) is a valuable device converting the waste heat into electricity which can be collected and stored for electronics. In this paper, the thermal energy from human body is captured and converted to the low electrical energy by means of thermoelectric energy harvester. The aim of presented work is utilizing the converted electricity to power the related electronic device and to extend the working life of a sensor node. Considering the related characteristics of TEG used for human, a type of a novel power management system is designed and presented to harvest generated electricity. The proposed circuit is developed based on off-the-shelf commercial chips, LTC3108 and BQ25504. It can accept the lowest input voltage of 20 mV, which is more suitable for human thermoelectric energy harvesting. Through experiments, developed energy harvesting system can effectively power the sensor to intermittently transmit the data as well as perform the converted energy storage. Compared to the independent commercial chips applications and other microcontroller-based energy harvesting systems, the designed thermoelectric energy harvester system presents the advantages not only in high energy storage utilization rate but also the ultra-low input voltage characteristic. Since the heat from human body is harvested, therefore, the system can possibly be used to power the sensor placed on human body and has practical applications such as physiological parameter monitoring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".