Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Can On Body Sensing Be Spatial Adaptive?

Shubham Rohal, Dong Yoon Lee, Phuc Nguyen, Shijia Pan

TL;DR

This work tackles the rigidity of fixed on-body sensor placement by introducing GearUp, a surface-structure–assisted, gear-based framework that enables 2D mobility of sensors on the body. It combines a mounting and transfer mechanism, a gear-matrix motion transfer system, and a parity-aware path-planning approach to coordinate multiple sensors under structural constraints. The authors validate the concept with a real-world prototype and simulations up to a $9 \times 9$ gear matrix supporting as many as 16 concurrent sensors, demonstrating feasible per-step timings and near-optimal path efficiency. The approach promises enhanced spatial coverage and personalization in wearable sensing, with implications for diverse physiological monitoring tasks.

Abstract

Wearable sensors are typically affixed to specific locations on the human body, and their position remains static, only changing unintentionally due to motion artifacts. This static configuration introduces significant limitations. As a result, current systems miss the opportunity to capture dynamic physiological data from diverse body regions. This research investigates the potential of developing movable sensors that adaptively reposition themselves to sample different areas of interest on the body, addressing gaps in spatial coverage. We designed, developed, and fabricated a 3 x 3 matrix platform to support moving sensors from one location to another. We validated the feasibility through simulations on a matrix of up to 9 x 9 locations with up to 16 concurrent sensors and real-world prototype characterization.

Can On Body Sensing Be Spatial Adaptive?

TL;DR

This work tackles the rigidity of fixed on-body sensor placement by introducing GearUp, a surface-structure–assisted, gear-based framework that enables 2D mobility of sensors on the body. It combines a mounting and transfer mechanism, a gear-matrix motion transfer system, and a parity-aware path-planning approach to coordinate multiple sensors under structural constraints. The authors validate the concept with a real-world prototype and simulations up to a gear matrix supporting as many as 16 concurrent sensors, demonstrating feasible per-step timings and near-optimal path efficiency. The approach promises enhanced spatial coverage and personalization in wearable sensing, with implications for diverse physiological monitoring tasks.

Abstract

Wearable sensors are typically affixed to specific locations on the human body, and their position remains static, only changing unintentionally due to motion artifacts. This static configuration introduces significant limitations. As a result, current systems miss the opportunity to capture dynamic physiological data from diverse body regions. This research investigates the potential of developing movable sensors that adaptively reposition themselves to sample different areas of interest on the body, addressing gaps in spatial coverage. We designed, developed, and fabricated a 3 x 3 matrix platform to support moving sensors from one location to another. We validated the feasibility through simulations on a matrix of up to 9 x 9 locations with up to 16 concurrent sensors and real-world prototype characterization.
Paper Structure (23 sections, 2 equations, 7 figures)

This paper contains 23 sections, 2 equations, 7 figures.

Table of Contents

  1. Introduction
  2. Proposed Approach
  3. Spatial Adaptive Wearable via Surface Structure-Assistive Sensor Movement
  4. GearUp : Surface Structure-Assistive Design with Gears
  5. Mounting Mechanism
  6. Along Rail Sensor Transfer Mechanism
  7. Gear Matrix Motion Transfer
  8. Surface Structure-Constrained Path Planning
  9. Gear Channel Parity
  10. Sensor Capacity
  11. Path Planning with Parity Constraints
  12. Preliminary Evaluation
  13. Prototype Characterization
  14. Gear Matrix Size Analysis
  15. Sensor Population Analysis
  16. ...and 8 more sections

Figures (7)

  • Figure 1: Wearable application examples with the needs of spatial adaptation.
  • Figure 2: Existing platforms for movable sensors have limitations and are not directly applicable to wearable systems.
  • Figure 3: GearUp design overview. (a) individual gear design with radius of $r$ and channel number of $C$. (b) rail sensor transfer mechanism. (c) gear matrix of size $M \times N$.
  • Figure 4: GearUp implementation with 4 channels.
  • Figure 5: Example path planning (from $S$ to $E$) and sensor/gear movement.
  • ...and 2 more figures

Theorems & Definitions (4)

  • Definition 1: $G_2$
  • Definition 2: checkerboard pattern
  • proof : In-Place Transfer
  • proof