Long-Term Stress Estimation Using Work-Period Baseline Segmentation
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Solution Overview
Problem
Existing methods for estimating long-term stress in office workers are inaccurate due to individual differences in autonomic nerve indices, particularly when users experience stress at the start or end of work, as they often cannot remain calm and restful, leading to unreliable baseline calculations.
Innovation Solution
A stress estimation apparatus that detects work start and end times, calculates short-term stress values from biometric waveforms, removes baselines using working-hours values, and estimates long-term stress based on stress fluctuation ranges, using regression models and subjective evaluation values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If baseline is calculated from pre-work short-term stress value, then long-term stress estimation is simplified, but estimation accuracy deteriorates when user experiences stress at work start or end
Solution Approach 1:
The patent segments the working period into multiple measurement intervals (first period, second period, third period) with different baseline handling strategies. The first and third periods use baseline removal, while the second period uses raw short-term stress values, allowing accurate capture of stress at work transitions without compromising overall estimation accuracy.
Solution Approach 2:
The patent dynamically adjusts the stress estimation approach based on the measurement period. Different calculation methods are applied to different time periods within the working hours, making the system adaptive to the user's stress patterns at various stages of work rather than using a static baseline approach.
2Measurement precision
If short-term stress values are used directly without baseline removal, then stress at work start and end is captured accurately, but individual differences cause estimation errors
Solution Approach 1:
The patent divides the measurement into segments where baseline removal is applied selectively to certain periods (first and third periods) while other periods (second period) use raw values. This segmentation allows the system to benefit from both baseline-adjusted measurements for consistency and raw measurements for capturing acute stress responses.
Solution Approach 2:
Different processing qualities are applied to different time periods. The first and third periods undergo baseline removal processing to account for individual differences, while the second period maintains raw quality to preserve acute stress information, optimizing the overall estimation reliability.
Data Source
AI summary
According to one embodiment, a stress estimation apparatus includes a processing circuit and an acquirer. The processing circuit detects at least one of a work start time and a work end time for a user performing work during working hours. The processing circuit calculates, based on the detected result, a short-term stress value from a biometric waveform of the user acquired by the acquirer. The processing circuit calculates a baseline based on at least the working-hours short-term stress value. The processing circuit calculates a stress fluctuation range of the user by removing the baseline from the short-term stress value. The processing circuit estimates, based on the stress fluctuation range, a magnitude of long-term stress of the user.