Traditional wearable sensors are limited in how well they track physical activity or health data. Most focus on one function—like counting steps or measuring heart rate—and often require rigid devices or uncomfortable attachments. They struggle to provide a complete, accurate picture of someone’s movements, posture, environment, or even interactions with others.
The Solution
This invention introduces a smart clothing system using fabric-based capacitive sensors. These sensors are seamlessly built into everyday items—like shirts, pants, shoes, socks, insoles, or even objects like beds, chairs, steering wheels, and sports equipment. They measure tiny changes in electric charge (capacitance) to detect body movement, posture, breathing, environmental changes (like humidity or temperature), and even interactions between people or with objects.
What’s New
Unlike other systems, this invention uses a network of sensors across multiple garments and objects to gather and compare data at once—giving a full-body, real-time understanding of behaviour and surroundings. It can distinguish between standing and sitting, walking and running, breathing through the nose or mouth, and whether a person is smoking, eating, or even interacting with another person or pet. It can also detect environmental changes, like walking on grass vs. tile, or whether someone is inside or outside.
Tangible Benefits
- Monitors breathing, posture, and movement without discomfort.
- Enables personalized health insights—e.g., signs of sleep issues or stress.
- Tracks daily habits to encourage better lifestyle choices.
- Detects abnormal behaviour patterns, such as falls or irregular breathing.
- Enhances sports training by showing how force and motion interact with objects like balls or skis.
Broader Impact
This system could transform healthcare, fitness, elderly care, sports, and smart living. It helps people understand their bodies better, encourages healthy behaviours, and could support remote diagnostics or real-time coaching—while collecting data that improves AI-driven behavioural models over time.