Ergonomic Recommendation System for Computing Devices
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing ergonomic solutions for digital device use are inefficient as they do not consider all crucial factors and fail to adapt to different devices, leading to improper user postures and potential health issues like Musculoskeletal Disorders and back pain.
Innovation Solution
A recommendation system that monitors and compares pre-defined position parameters of users, identifying deviations and providing real-time recommendations for correcting them, using data from various sensors and user profiles to ensure optimal ergonomic positioning across different devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing ergonomic techniques provide correct sitting postures based on a few factors, then the system is simple to implement, but it fails to determine accurate ergonomic positions and does not consider all crucial factors
Solution Approach 1:
The system segments the ergonomic monitoring into multiple independent components: user identification module, profile extraction module, critical area identification module, position parameter monitoring module, comparison module, and recommendation module. Each component handles a specific aspect of the ergonomic assessment, allowing the system to consider multiple crucial factors (user characteristics, device type, environmental conditions) without becoming an unmanageable monolithic system.
Solution Approach 2:
The system creates a universal ergonomic recommendation framework that can adapt to different user types, multiple digital devices (computers, laptops, mobile phones), and various critical body areas. By storing user profiles in a repository and using device-agnostic monitoring of position parameters, the system provides comprehensive ergonomic guidance across diverse scenarios while maintaining a consistent architectural structure.
2Adaptability or versatility
If existing techniques are restricted to a single device for a particular user, then the system is simple to manage, but it cannot identify users or recommend ergonomic positions when they switch to different devices
Solution Approach 1:
The system implements self-service through automatic user identification and profile retrieval. When a user approaches or uses a device, the system automatically identifies the user (through sensors or login), extracts their profile from the repository, and applies their specific ergonomic parameters without requiring manual setup or configuration. This enables seamless cross-device ergonomics while keeping the user experience simple.
Solution Approach 2:
The user profile repository acts as an intermediary layer between users and devices. Instead of managing ergonomic settings directly on each device, the system stores comprehensive user profiles (including physical characteristics, preferences, and health considerations) in a centralized repository. This intermediary enables any device to access and apply the correct ergonomic parameters for any user, facilitating cross-device adaptability without increasing per-device complexity.
3Reliability
If users follow ergonomic practices manually, then they can maintain proper posture, but they may fail to do so consistently resulting in health issues
Solution Approach 1:
The system implements continuous feedback by monitoring user position parameters in real-time and comparing them against the user's profile and ergonomic standards. When deviations are detected (such as incorrect sitting posture, improper device positioning, or awkward wrist angles), the system immediately provides corrective recommendations to the user. This closed-loop feedback mechanism ensures consistent ergonomic maintenance without relying on user memory or willpower, while the automation level remains appropriate for the context.
Solution Approach 2:
The system performs preliminary actions by pre-configuring user profiles with optimal ergonomic parameters based on their physical characteristics and device usage patterns. Before the user even begins working, the system has already determined the correct sitting height, device distance, screen angle, and other critical parameters. This preliminary setup enables reliable ergonomic guidance while keeping the real-time monitoring and recommendation system relatively simple.
Data Source
AI summary
The present disclosure relates to a method and system for recommending optimal ergonomic position for a user of a computing device by a recommendation system. The recommendation system receives user data from one or more data sources and extracts a profile of the user from a repository based on the user data. The recommendation system identifies one or more critical areas of the user, where each of the critical areas are associated with a plurality of pre-defined position parameters and also monitor the plurality of pre-defined position parameters of the user to determine corresponding values. The recommendation system compare the values of the plurality of pre-defined position parameters with predefined values of the pre-defined position parameters and identify deviations in one or more of the plurality of pre-defined position parameters based on the comparison and provide recommendations for correcting the deviations from the pre-defined position parameters to the user.