Next-generation Internet of Medical Things (IoMT) must balance efficient local decision-making with strong privacy protection for remote monitoring and diagnosis. However, the resource constraints of IoMT devices make this difficult. This paper introduces a novel bio-signal encoder within the hyperdimensional computing (HDC) framework. It leverages inherent transistor variations for physically unclonable encoding. This technique is called variation-based analog entropy (VAE). VAE reduces memory footprint and power consumption while enhancing security. It offers a scalable, energy-efficient solution that addresses IoT’s resource limitations while ensuring secure, intelligent healthcare applications.The VAE cell achieves high entropy robustness (30.23-57.76 dB signal-to-noise ratio) with only a 10-transistor footprint. It reduces HDC vector dimensions by 14.3× and improves accuracy by 2%. Compared to an SRAM baseline, it shrinks encoder area by 1.3-4.4× and cuts leakage power by 327×. Custom analog circuits for entropy management eliminate data conversion, boosting energy efficiency to 48.5 nJ per query. Evaluations on experimentally collected bio-fluid data demonstrate classification accuracy of 94.9 % and 97.6 % in 5-class and 3-class viscosity sensing, respectively. This highlights the potential of the system for IoMT applications. Furthermore, the VAE significantly enhances security, lowering attacker-restored data peak SNR by 16 dB, and making unauthorized recovery indistinguishable.

This paper proposes a method of simultaneously calibrating magnetic localization and actuation systems for magnetically actuated robots. In this method, uncalibrated magnetic localization and actuation systems are calibrated simultaneously with minimal human intervention, which enables self-calibration, flexible reconfiguration, and long-term correctness of the system parameters. This method employs a bundle adjustment framework using a quadratic measurement model for sensors and the magnetic dipole model for actuators. The proposed method has been verified in comparison with finite element simulations and existing calibration methods for magnetic actuators and sensor arrays. In the experiments, the determinant of coefficient (R2value) was 99.84% for the sensor system and 99.45% for the actuator system after the calibration, comparable with individual state-of-art calibration methods of calibrating magnetic actuators and sensor arrays. This method has potential to improve the reconfigurability and long-term accuracy of magnetic robot localization and actuation systems, such as magnetically actuated capsule endoscopes.

Most demonstrated mobile microrobot tasks so far have been achieved via pick-and-placing and dynamic trapping with teleoperation or simple path following algorithms. In our previous work, an untethered magnetic microgripper has been developed which has advanced functions, such as gripping objects. Both teleoperated manipulation in 2D and 3D have been demonstrated. However, it is challenging to control the magnetic microgripper to carry out manipulation tasks, because the grasping of objects so far in the literature relies heavily on teleoperation, which takes several minutes with even a skilled human expert. Here, we propose a new spin-walking locomotion and an automated 2D grasping motion planner for the microgripper, which enables time-efficient automatic grasping of microobjects that has not been achieved yet for untethered microrobots. In its locomotion, the microgripper repeatedly rotates about two principal axes to regulate its pose and move precisely on a surface. The motion planner could plan different motion primitives for grasping and compensate the uncertainties in the motion by learning the uncertainties and planning accordingly. We experimentally demonstrated that, using the proposed method, the microgripper could align to the target pose with error less than 0.1 body length and grip the objects within 40 seconds. Our method could significantly improve the time efficiency of micro-scale manipulation and have potential applications in microassembly and biomedical engineering.