Design and Implementation of a Semi-Autonomous Quadruped Assistive Robot for Remote Elderly Monitoring and COVID-19 Response
Bibliographic record
Abstract
This paper presents the design and development of an assistive quadruped robot capable of autonomous or semi-autonomous interaction in healthcare settings, with a focus on elderly care and infectious disease environments. The robot leverages low-cost, off-the-shelf components-including an ESP32-CAM module [1] for real-time vision, an MLX90614 infrared sensor for thermal sensing [2], and MG90S micro servos [3] for locomotion—demonstrating that affordable hardware can support autonomous healthcare robotics applications. The system supports multiple functional modes, including autonomous follow mode, manual control, and measurement of body temperature at a safe distance. The server-side interface allows the user to view real-time video, control the robot, and monitor live health-related data. Three key implementation aspects were demonstrated to validate the system's usability: UART-based inter-device communication [4], modular hardware design using 3D printing [5], and a custom-designed printed circuit board (PCB) developed using KiCad [6] to integrate the ESP32 chipset [1], camera, distance sensor, and MLX90614 temperature scanner [2] into a small form factor-while enabling the user full control of the robot; and automatic object tracking of our reconnaissance model in a compact form factor using TensorFlow Lite [7]. The prototype demonstrated strong capability both as an indoor robot, with a modular design that allows it to be reconfigured and used for broader and alternative use cases, including pandemic monitoring and remote elder care. This research project advances low-cost utilization of assistive robots intended to improve healthcare safety and quality of living. By directly addressing urgent need for accessible and affordable healthcare technology, this work contributes to the public good by expanding the reach of advanced care solutions to broader communities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".