scholarly journals Inattentive Driving Detection Using Body-Worn Sensors: Feasibility Study

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 352
Author(s):  
Takuma Akiduki ◽  
Jun Nagasawa ◽  
Zhong Zhang ◽  
Yuto Omae ◽  
Toshiya Arakawa ◽  
...  
Keyword(s):  
Heart Rate ◽  
Lower Cost ◽  
Driving Simulator ◽  
Inertial Sensors ◽  
Internal State ◽  
Driver Behavior ◽  
Body Movement ◽  
Detection Algorithm ◽  
Rate Sensor

This study aims to build a system for detecting a driver’s internal state using body-worn sensors. Our system is intended to detect inattentive driving that occurs during long-term driving on a monotonous road, such as a high-way road. The inattentive state of a driver in this study is an absent-minded state caused by a decrease in driver vigilance levels due to fatigue or drowsiness. However, it is difficult to clearly define these inattentive states because it is difficult for the driver to recognize when they fall into an absent-minded state. To address this problem and achieve our goal, we have proposed a detection algorithm for inattentive driving that not only uses a heart rate sensor, but also uses body-worn inertial sensors, which have the potential to detect driver behavior more accurately and at a much lower cost. The proposed method combines three detection models: body movement, drowsiness, and inattention detection, based on an anomaly detection algorithm. Furthermore, we have verified the accuracy of the algorithm with the experimental data for five participants that were measured in long-term and monotonous driving scenarios by using a driving simulator. The results indicate that our approach can detect both the inattentive and drowsiness states of drivers using signals from both the heart rate sensor and accelerometers placed on wrists.

Prediction of Menstrual Cycle Phase by Wearable Heart Rate Sensor

2021 ◽  
pp. 1-15
Author(s):  
Junichiro Hayano ◽  
Emi Yuda
Keyword(s):  
Heart Rate ◽  
Menstrual Cycle ◽  
Technological Development ◽  
Unmet Need ◽  
Cycle Phase ◽  
Rate Data ◽  
Menstrual Cycle Phase ◽  
Heart Rate Data ◽  
Rate Sensor

The prediction of the menstrual cycle phase and fertility window by easily measurable bio-signals is an unmet need and such technological development will greatly contribute to women's QoL. Although many studies have reported differences in autonomic indices of heart rate variability (HRV) between follicular and luteal phases, they have not yet reached the level that can predict the menstrual cycle phases. The recent development of wearable sensors-enabled heart rate monitoring during daily life. The long-term heart rate data obtained by them carry plenty of information, and the information that can be extracted by conventional HRV analysis is only a limited part of it. This chapter introduces comprehensive analyses of long-term heart rate data that may be useful for revealing their associations with the menstrual cycle phase.

Long-Term Monitoring of Heart Rate, Respiration Rhythm, and Body Movement During Sleep Based upon a Network

2010 ◽  
Vol 16 (2) ◽  
pp. 244-253 ◽  
Author(s):  
Xin Zhu ◽  
Wenxi Chen ◽  
Tetsu Nemoto ◽  
Kei-ichiro Kitamura ◽  
Daming Wei
Keyword(s):  
Heart Rate ◽  
Body Movement ◽  
Respiration Rhythm ◽  
Long Term Monitoring ◽  
Term Monitoring

scholarly journals Temporal contribution of body movement to very long-term heart rate variability in humans

2000 ◽  
Vol 278 (4) ◽  
pp. H1035-H1041 ◽  
Author(s):  
Naoko Aoyagi ◽  
Kyoko Ohashi ◽  
Shinji Tomono ◽  
Yoshiharu Yamamoto
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Wigner Distribution ◽  
Body Movement ◽  
Ambulatory Monitoring ◽  
Coarse Graining ◽  
Single Subject ◽  
Activity Levels ◽  
Time Frequency

A newly developed, very long-term (∼7 days) ambulatory monitoring system for assessing beat-to-beat heart rate variability (HRV) and body movements (BM) was used to study the mechanism(s) responsible for the long-period oscillation in human HRV. Data continuously collected from five healthy subjects were analyzed by 1) standard auto- and cross-spectral techniques, 2) a cross-Wigner distribution (WD; a time-frequency analysis) between BM and HRV for 10-s averaged data, and 3) coarse-graining spectral analysis for 600 successive cardiac cycles. The results showed 1) a clear circadian rhythm in HRV and BM, 2) a 1/ f β-type spectrum in HRV and BM at ultradian frequencies, and 3) coherent relationships between BM and HRV only at specific ultradian as well as circadian frequencies, indicated by significant ( P < 0.05) levels of the squared coherence and temporal localizations of the covariance between BM and HRV in the cross-WD. In a single subject, an instance in which the behavioral (mean BM) and autonomic [HRV power >0.15 Hz and mean heart rate (HR)] rhythmicities were dissociated occurred when the individual had an irregular daily life. It was concluded that the long-term HRV in normal humans contained persistent oscillations synchronized with those of BM at ultradian frequencies but could not be explained exclusively by activity levels of the subjects.

scholarly journals Photoplethysmographic Time-Domain Heart Rate Measurement Algorithm for Resource-Constrained Wearable Devices and its Implementation

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1783 ◽  
Author(s):  
Marek Wójcikowski ◽  
Bogdan Pankiewicz
Keyword(s):  
Heart Rate ◽  
Time Domain ◽  
Body Movement ◽  
Real Life ◽  
Ground Truth ◽  
Skin Surface ◽  
Detection Algorithm ◽  
Resource Constrained ◽  
Peak Detection Algorithm

This paper presents an algorithm for the measurement of the human heart rate, using photoplethysmography (PPG), i.e., the detection of the light at the skin surface. The signal from the PPG sensor is processed in time-domain; the peaks in the preprocessed and conditioned PPG waveform are detected by using a peak detection algorithm to find the heart rate in real time. Apart from the PPG sensor, the accelerometer is also used to detect body movement and to indicate the moments in time, for which the PPG waveform can be unreliable. This paper describes in detail the signal conditioning path and the modified algorithm, and it also gives an example of implementation in a resource-constrained wrist-wearable device. The algorithm was evaluated by using the publicly available PPG-DaLia dataset containing samples collected during real-life activities with a PPG sensor and accelerometer and with an ECG signal as ground truth. The quality of the results is comparable to the other algorithms from the literature, while the required hardware resources are lower, which can be significant for wearable applications.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

Effect of heart rate on long-term mortality among men and women

2007 ◽  
Vol 62 (3) ◽  
pp. 271-275 ◽  
Author(s):  
H. THEOBALD ◽  
P.E. WÄNDELL
Keyword(s):  
Heart Rate ◽  
Men And Women ◽  
Long Term Mortality

EMFi material as wearable heart rate sensor for night time recordings

2010 ◽  
Author(s):  
Antti Vehkaoja ◽  
Timo Salpavaara ◽  
Jarmo Verho ◽  
Jukka Lekkala
Keyword(s):  
Heart Rate ◽  
Night Time ◽  
Rate Sensor
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