Wireless Sensor System for Activity Recognition and Energy Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current wearable wireless sensors lack the capability to effectively monitor and recognize diverse motion activities across various objects, including humans, animals, and environmental conditions, with limited impact on improving daily life and lacking in real-time feedback and energy efficiency.
Innovation Solution
A wireless sensor system with a housing for attachment to objects, incorporating motion activity detectors and a processor for data processing, connected via a network for activity pattern recognition, using Euler angles and Quaternion angles to determine sensor locations, and employing ultra-low power consumption with dynamic configuration for extended battery life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wearable wireless sensors continuously monitor and process motion activity data in real-time, then activity recognition accuracy and real-time feedback capability are improved, but energy consumption increases
Solution Approach 1:
The system segments activity recognition into two stages: (1) local preprocessing and feature extraction at the sensor node using accelerometers and gyroscopes, and (2) cloud-based pattern recognition using HMM and SVM algorithms. This segmentation allows real-time feedback with low power consumption at the wearable device while maintaining high accuracy through sophisticated cloud processing.
Solution Approach 2:
The sensor system employs periodic sampling of motion data at optimized intervals rather than continuous monitoring. The accelerometers and gyroscopes capture motion events at specific time points, reducing overall energy consumption while maintaining sufficient data density for accurate activity recognition through the cloud-based algorithms.
2Productivity
If the sensor system processes and transmits detailed activity data continuously, then real-time feedback and activity pattern recognition are improved, but battery life decreases
Solution Approach 1:
The system extracts only essential motion features and event data from raw accelerometer and gyroscope signals for transmission to the cloud. Local preprocessing filters out redundant information, transmitting only significant activity patterns and extracted features, thereby reducing communication energy consumption while maintaining real-time feedback capability.
Solution Approach 2:
The cloud-based server acts as an intermediary that performs sophisticated activity pattern recognition using HMM and SVM algorithms. This mediator handles the computationally intensive processing tasks, allowing the wearable sensor to maintain real-time feedback with minimal local processing and extended battery life.
3Measurement precision
If the sensor uses sophisticated algorithms for activity pattern recognition, then recognition accuracy is improved, but computational complexity and power consumption increase
Solution Approach 1:
The system segments computational tasks between the wearable sensor and cloud server. The sensor performs simple local preprocessing of accelerometer and gyroscope data, while the cloud server executes sophisticated HMM and SVM algorithms for activity pattern recognition. This segmentation achieves high recognition accuracy without requiring complex processing at the resource-constrained wearable device.
4Adaptability or versatility
If the sensor monitors multiple types of motion activities across various objects, then versatility and application range are improved, but device complexity and energy consumption increase
Solution Approach 1:
The system employs universal accelerometer and gyroscope sensors that can detect motion patterns across multiple object types (humans, animals, birds, aquatic organisms, plants, buildings, machines). The cloud-based HMM and SVM algorithms provide universal activity pattern recognition capabilities that adapt to different objects and environments, achieving high versatility without increasing hardware complexity at the sensor level.
Data Source
AI summary
A computer-implemented method for recognizing a user's activity pattern includes pre-storing activity data in a computer system, automatically determining locations of one or more sensors on a user's body, obtaining time series of measured activity parameters by the one or more sensors, automatically segmenting the time series of measured activity parameters into two or more activity periods, determining a spatial range of the movement in an activity period, and recognizing an activity in the activity period based at least in part on the measured activity parameters and the pre-stored activity data.