Streaming Analytics for Human Body Movement Data
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Solution Overview
Problem
Existing wearable devices and smart watches have limitations in processing and analyzing large amounts of streaming data from various sensors, lacking the capability to provide comprehensive feedback on user movement and health metrics, such as injury detection and fatigue analysis, due to their limited processing power and connectivity.
Innovation Solution
Implementing a system that uses machine learning algorithms and wearable devices with integrated sensors to collect and analyze human body movement data, classifying movements and providing feedback through streaming analytics, which can be scaled for processing large data streams and updated based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If wearable devices collect and process large amounts of streaming data from multiple sensors, then the comprehensiveness of health monitoring and movement analysis is improved, but the device's power consumption increases and processing capabilities are overwhelmed
Solution Approach 1:
The system segments processing tasks between the wearable device (data collection and preliminary processing) and remote servers (complex analytics and machine learning). This allows comprehensive health monitoring without requiring the wearable device to have high processing power, thus reducing its power consumption while maintaining data comprehensiveness.
Solution Approach 2:
A mobile device or gateway acts as an intermediary between the wearable sensor and the remote server. The wearable device transmits raw or pre-processed data to this intermediary, which then communicates with the server for advanced analytics. This architecture enables comprehensive monitoring while distributing the computational burden and reducing the power requirements of the wearable device.
2Measurement precision
If wearable devices perform comprehensive real-time analysis of movement data, then the accuracy of injury detection and fatigue analysis is improved, but the device complexity increases
Solution Approach 1:
The system divides the analysis pipeline into multiple stages: basic motion capture by sensors, preliminary processing at the wearable or mobile device, and advanced machine learning analysis on remote servers. This segmentation enables high accuracy in injury detection while keeping the wearable device relatively simple.
Solution Approach 2:
The remote server acts as an intermediary that performs complex analytical functions. The wearable device collects data and transmits it through a mobile device to the server, which executes sophisticated machine learning models for accurate injury and fatigue detection. This approach achieves high measurement precision without requiring the wearable device itself to be complex.
3Speed
If the system processes streaming data locally on the wearable device, then the real-time responsiveness is improved, but the device requires higher processing power and energy consumption
Solution Approach 1:
The system segments real-time requirements into two parts: immediate local processing for critical functions (maintaining responsiveness) and deferred cloud processing for non-critical analytics. This allows the system to achieve real-time responsiveness for essential functions without requiring the wearable device to have high sustained processing power.
Solution Approach 2:
The system implements periodic data transmission and processing cycles. Critical data is processed in real-time at the edge device, while less time-sensitive data is batched and transmitted periodically to the cloud for analysis. This periodic approach reduces the continuous processing power requirements while maintaining adequate real-time responsiveness.
Data Source
AI summary
Large amounts of human body movement data may be collected, possibly via streaming data, from one or more sensors worn by a user. The data may be analyzed along with other classification data to generate feedback for the user or for other interested people (e.g., a trainer, a coach, a team member, health professional, etc.). The analysis may utilize one or more machine learning (ML) algorithms that use training data to create one or more ML models. When a user is evaluated after receiving feedback, accuracy of the feedback may be evaluated and fed back to the ML model to continue training the ML model(s).