Activity Classification via Multi-Parameter Contextualization
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Solution Overview
Problem
Current exercise and activity monitoring devices fail to accurately interpret data and provide effective coaching feedback, leading to inefficient workouts and potential injuries due to the reliance on single parameter classifications like heart rate zones, which do not account for multiple parameters and contextual changes during exercise.
Innovation Solution
A method and system that utilize multi-parameter contextualization to classify activities, comparing real-time data from various parameters such as heart rate, speed, altitude, and resistance to generate personalized training plans and provide real-time coaching advice, allowing for automated modifications to future activity sessions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If single parameter classification (e.g., heart rate zones) is used for activity monitoring, then device complexity is reduced and ease of operation is improved, but measurement precision and reliability of activity classification deteriorate
Solution Approach 1:
The patent transitions from single-parameter classification (heart rate zones only) to multi-parameter classification by incorporating additional parameters such as speed, distance, duration, and terrain type. This allows the system to differentiate between activities more accurately (e.g., distinguishing running from cycling at the same heart rate) while maintaining automated operation through algorithmic processing of multiple data streams.
Solution Approach 2:
The patent creates a composite classification system that integrates multiple parameter types (physiological, kinematic, environmental) into a unified activity classification framework. This composite approach combines heart rate data with motion sensors, GPS information, and terrain data to produce more reliable activity identification than any single parameter could achieve alone.
2Measurement precision
If multi-parameter contextualization is used for activity classification, then measurement precision and reliability are improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent segments the activity classification process into distinct functional modules: data acquisition from multiple sensors, parameter normalization, contextual rule evaluation, and activity type determination. This segmentation allows each module to handle specific tasks independently, reducing overall system complexity while maintaining multi-parameter classification capabilities.
Solution Approach 2:
The patent introduces an intermediary processing layer that acts as a mediator between raw sensor data and final activity classification. This intermediary layer applies contextual rules and algorithms to integrate multiple parameters, effectively managing the complexity of multi-parameter analysis while producing simplified activity type outputs that are easy to interpret and act upon.
3Productivity
If automated activity interpretation and coaching feedback are implemented, then productivity and fitness outcomes are improved, but loss of information increases due to the complexity of data processing
Solution Approach 1:
The patent implements a feedback mechanism where activity classification results and performance metrics are automatically translated into actionable coaching feedback. The system monitors multiple parameters in real-time, compares them against target values and historical data, and provides immediate feedback on activity intensity, duration, and type, enabling users to optimize their workouts without manual data analysis.
Solution Approach 2:
The patent enables the system to automatically interpret its own multi-parameter data and generate coaching recommendations without requiring user expertise in data analysis. The automated interpretation engine processes complex multi-parameter datasets, identifies performance patterns, and delivers self-service coaching feedback that helps users improve fitness outcomes independently.
Data Source
AI summary
The technology disclosed here involves classifying activity data based on inactivity types. An example method involves receiving, by a processor, activity data of one or more sensors, the activity data comprising a plurality of sensor measurements associated with multiple parameters monitored during an activity session of a user; retrieving a set of criteria that comprises thresholds to detect a plurality of inactivity types, wherein the set comprises a criterion related to a physical activity; classifying, by the processor based on the set of criteria, the received activity data of the one or more sensors into one or more segments, wherein the one or more segments comprise a segment comprising a portion of the activity data corresponding to an inactivity type of the plurality of inactivity types; and generating output based on the classifying.