Future Work
Planned enhancements and research directions to evolve SilverHand from proof-of-concept to validated assistive device.
Multichannel EMG
Expand from single-channel to 4–8 channel EMG sensing to enable independent finger control and grasp pattern recognition.
Implementation Details:
- •Additional INA128 channels with multiplexed ADC
- •Higher-resolution ADC (12–16 bit) for improved SNR
- •Feature extraction (RMS, mean frequency) for classification
ML-Based Intent Classification
Replace threshold-based control with machine learning model trained on user-specific EMG patterns for grasp types (power grip, pinch, etc.).
Implementation Details:
- •Dataset collection: record EMG for common grasp intents
- •Lightweight classifier (e.g., LDA, SVM) running on Pi Zero
- •Real-time inference latency target: <20ms
Product-Level Enclosure
Design injection-molded or thermoformed enclosure for improved aesthetics, durability, and comfort.
Implementation Details:
- •Ergonomic strap system with padding
- •IP54 rating for moisture/dust resistance
- •Integrated battery compartment with charging port
Clinical Testing & Validation
Conduct user studies with arthritis patients to validate functional benefit, safety, and usability.
Implementation Details:
- •IRB approval for human subjects research
- •Standardized grip strength and ADL (activities of daily living) assessments
- •Longitudinal study (4–8 weeks) to assess real-world adoption
Additional Considerations
- •Haptic feedback: Integrate vibration motor to provide tactile confirmation of grasp state (open/closed).
- •Wireless connectivity: Bluetooth Low Energy for mobile app integration (calibration, battery monitoring, usage analytics).
- •Customization service: Online configurator for hand size, finger lengths, and material preferences (3D printing on demand).
- •Cost reduction: Target bill of materials below $150 through volume purchasing and design-for-manufacturing optimization.
Research Alignment
Future development of SilverHand aligns with global research in biomechatronics and assistive robotics. Key research themes that guide our work include:
- User-centered design for wearable robotics and accessibility
- Real-time biosignal processing and intent decoding
- Accessible, low-cost assistive technology through open-source design
- Human-robot interaction and transparent, intuitive control
Collaboration opportunities are welcomed for clinical trials, advanced control algorithms, and translational research toward broader accessibility and impact.