Fitness Tracker Stride Estimation Under Arm Constraint
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Solution Overview
Problem
Fitness tracking devices inaccurately estimate stride length and fitness data when a user's arm is constrained, such as when pushing a stroller, leading to underestimation of work performed and incorrect fitness metrics.
Innovation Solution
The device collects motion data to detect arm constraint, estimates stride length using historical step cadence-to-stride length data, and calculates fitness data, including distance, speed, and caloric expenditure, while accounting for increased load when pushing an object by analyzing pose angle and accelerometer energy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fitness tracking devices use arm swing motion to determine stride length, then stride length can be accurately measured during normal walking, but measurement precision deteriorates when the user's arm is constrained
Solution Approach 1:
The system changes the measurement parameters from direct arm swing analysis to alternative parameters including step cadence, historical stride length data, and accelerometer energy patterns. By substituting the constrained arm swing parameter with these alternative parameters, the system maintains measurement capability under constrained conditions while preserving accuracy during normal walking.
Solution Approach 2:
The system introduces intermediary calculations including step cadence-to-stride length relationships and accelerometer energy analysis as mediators between the constrained arm motion and stride length determination. These intermediaries allow the system to infer stride length indirectly when direct measurement is compromised, resolving the contradiction between maintaining measurement precision and adapting to constrained conditions.
2Productivity
If fitness tracking devices calculate work based on body mass and speed, then basic fitness data can be computed, but the calculated data does not account for increased work performed when pushing substantial loads
Solution Approach 1:
The system performs preliminary detection of constrained arm conditions and load pushing activities before finalizing fitness data calculations. By identifying these conditions in advance through accelerometer pattern recognition and pose angle analysis, the system can apply appropriate correction factors to the work calculation, ensuring both computational efficiency and measurement precision.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor accelerometer energy, pose angle, and motion patterns to detect when additional work is being performed. This feedback is then fed back into the fitness data calculation algorithm to adjust the work performed metrics, resolving the contradiction between maintaining simple calculation processes and achieving accurate work measurement under varying load conditions.
Data Source
AI summary
A system and method for collecting motion data using a fitness tracking device located on an arm of a user, detecting that the arm is constrained based on the motion data, estimating a stride length of the user based on the motion data and historical step cadence-to-stride length data, calculating fitness data using the estimated stride length, and outputting the fitness data to the user.