Wearable Activity Recognition Engine Using Sensor Location Context
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Solution Overview
Problem
Existing activity monitoring systems require users to be within range of a computing device for accurate data recording, leading to incomplete tracking of activities, especially when the device is moved or not worn in optimal positions, resulting in inaccurate readings.
Innovation Solution
A wearable activity tracking device equipped with embedded sensors and a sensor module that continuously receives real-time data from various sensors, including accelerometers, gyroscopes, and cameras, capable of determining its location and activity type regardless of its position on the body, using a body area module and activity module to analyze sensor inputs and transmit processed data wirelessly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the activity tracking device requires constant proximity to a computing device for data processing, then real-time activity recognition can be achieved, but the system fails to capture activities performed outside the computing device's range
Solution Approach 1:
The system divides the activity recognition function into two independent parts: (1) a wearable sensor module that collects raw sensor data locally, and (2) a computing device that processes the data. This segmentation allows the sensor module to operate independently and store data locally, ensuring continuous tracking regardless of proximity to the computing device.
Solution Approach 2:
The wearable device performs preliminary data collection and local storage of raw sensor data before it needs to be processed. By pre-collecting and caching data in local memory, the system ensures that no activity data is lost even when the computing device is out of range, and data can be processed later when connectivity is available.
2Ease of operation
If the activity tracking device is moved to different body locations, then user comfort and wearability are improved, but the accuracy of activity recognition deteriorates
Solution Approach 1:
The system dynamically adapts to different wear locations by using machine learning algorithms that are trained on sensor data from various body positions. The algorithm automatically adjusts its recognition patterns based on the actual wear location, allowing accurate activity recognition whether the device is worn on the wrist, ankle, or other body parts.
Solution Approach 2:
The system changes the parameters of the recognition algorithm based on detected wear location. By identifying which body part the device is worn on and adjusting the expected sensor signal patterns accordingly, the system maintains high accuracy across different wear positions without requiring manual reconfiguration.
3Loss of information
If manual activity logging is required, then complete activity data can be captured, but the time required for data entry increases significantly
Solution Approach 1:
The system performs automatic activity recognition and logging without requiring user intervention. The machine learning algorithm autonomously processes sensor data, identifies activities, and records them in the activity log, eliminating the need for manual data entry while maintaining complete and accurate activity tracking.
Data Source
AI summary
A real-time human activity recognition (rtHAR) engine embedded in a wearable device monitors a user's activities through the wearable device's sensors. The rtHAR uses the signals from the sensors to determine where the wearable device is relative to the user's body, and then determines the type of activity the user engages in depending upon the location of the wearable device relative to the user's body. The rtHAR is preferably installed on the wearable device as an embedded system, such as an operating system library or a module within software installed on the wearable device, so as to improve the quality of direct feedback from the wearable device to the user, and to minimize the amount of data sent from the wearable device to external archival and processing systems.