A recognition of human walking motion by using an accelerometer

Unuma, Munetoshi; Usami, Yoshiaki; Nonaka, Sakutaro

doi:10.1541/ieejfms1990.118.3_218

Cited by 1 publication

(1 citation statement)

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“…A screenshot is shown in Figure 5. This application also has functions to display three other indicators: acute stress, steps, and sleep time as implemented with reference to [11], [12], and [13], respectively. Combined with chronic stress, acute stress helps users learn what is causing their stress, while steps and sleep time are indices that indicate the effectiveness of, respectively, walking and sleeping, both of which are considered stress relievers.…”

Section: Stress Managementmentioning

confidence: 99%

Development of a Real-time Chronic Stress Visualization System from Long-term Physiological Data

Kitade

Tsujikawa

2022

2022 44th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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We have developed a real-time system which can estimate and display chronic stress levels determined from a long-term physiological data. It consists of wearable sensors that measure physiological data, a smartphone application that receives data from the sensors and displays chronic stress levels, and a cloud system that estimates them on the basis of received data. To operate it, we have to treat irregularly uploaded userphysiological-data of varying sizes, calculate chronic stress levels from long-term features without delay on a daily basis, and display them in real-time on the smartphone application. For this purpose, we have developed a system that requires relatively little memory and processing time with one six-hundredth of maximum memory usage and one twentieth of processing time as compared to conventional method by subdividing uploaded physiological data, calculating features from them, and creating long-term features by combining the subdivided features.

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Section: Stress Managementmentioning

confidence: 99%