Wearable Motion Sensor Synchronization for Pattern Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current motion recognition systems using wearable sensors struggle to accurately classify and provide feedback on motion patterns in real-time, especially when multiple sensors are involved, due to synchronization and processing challenges.
Innovation Solution
A method and system for synchronizing motion data from multiple wearable sensors using timestamp data, and employing machine-learning models to classify signature motion patterns, allowing for real-time feedback and analysis of motion primitives across various body parts and activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple wearable motion sensors are used to collect motion data, then measurement precision is improved, but device complexity and data synchronization difficulty increase
Solution Approach 1:
The system divides the motion data collection task across multiple independent wearable sensors (e.g., smartwatches, fitness trackers) positioned at different body locations. Each sensor independently collects motion data, which is then synchronized and processed as a unified dataset, enabling comprehensive motion pattern analysis without requiring a single complex sensor system
Solution Approach 2:
A cloud-based processing platform serves as an intermediary between multiple wearable sensors and the final motion pattern recognition. The platform receives data from various sensors, synchronizes timestamps, and processes the combined data to identify motion patterns, thereby managing system complexity centrally rather than requiring complex local processing at each sensor
2Measurement precision
If motion data from multiple sensors is synchronized using timestamps, then measurement precision is improved, but processing requirements and bandwidth increase
Solution Approach 1:
The system extracts and utilizes only the timestamp field from each sensor's motion data for synchronization purposes. By separating the timing information from the full motion data payload, the system can efficiently synchronize data across multiple sensors without transmitting or processing the complete data streams simultaneously, thereby reducing bandwidth and processing requirements
Solution Approach 2:
Timestamp-based synchronization is performed preliminarily before the main motion pattern classification processing. The system first aligns data from multiple sensors based on their timestamps, creating a synchronized dataset structure that simplifies subsequent processing steps and reduces the computational burden during actual motion pattern recognition
3Speed
If real-time motion pattern recognition is implemented, then responsiveness is improved, but processing requirements and energy consumption increase
Solution Approach 1:
The system performs partial motion pattern recognition locally at wearable devices using simplified classification algorithms, while reserving more computationally intensive processing for the cloud platform. This partial action approach enables real-time feedback at the wearable level without requiring the sensor to perform all processing tasks, thereby reducing energy consumption at the moving object while maintaining responsiveness
Solution Approach 2:
The cloud-based platform acts as an intermediary that handles complex motion pattern classification and comparison with reference data. Wearable sensors only perform lightweight data collection and preliminary processing, offloading the energy-intensive recognition tasks to the cloud where computational resources are abundant, thus maintaining real-time responsiveness while minimizing energy consumption at the wearable devices
Data Source
AI summary
Embodiments of the disclosed technology are directed to classifying motion data collected by wearable sensors. Motion data collected by a first wearable motion sensor and a second wearable motion sensor during the performance of an activity can be obtained. The motion data from the first wearable motion sensor can include data associated with one or more first motion primitives and the second motion data collected by the second wearable motion sensor can include data associated with one or more second motion primitives. The first motion data and the second motion data can be synchronized based at least in part on time stamp information. Data associated with a signature motion classification associated with the activity can be determined based at least in part on the one or more first motion primitives and the one or more second motion primitives.