A Review of Heartbeat Detection Systems for Automotive Applications

This paper aims to provide a review of the electrically assisted bicycles (also known as e-bikes) used for recovery of the rider’s physical and physiological information, monitoring of their health state, and adjusting the “medical” assistance accordingly. E-bikes have proven to be an excellent way to do physical activity while commuting, thus improving the user’s health and reducing air pollutant emissions. Such devices can also be seen as the first step to help unhealthy sedentary people to start exercising with reduced strain. Based on this analysis, the need to have e-bikes with artificial intelligence (AI) systems that recover and processe a large amount of data is discussed in depth. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used to complete the relevant papers’ search and selection in this systematic review.

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Smart Electrically Assisted Bicycles as Health Monitoring Systems: A Review

Avina-Bravo

Cassirame

Escriba

et al. 2022

“…Note that, in this study, we measured ECG data on the chests of participants. We could also acquire heart rate data by using wristwatch-type devices, such as the Apple Watch [ 39 ].…”

Section: Methodsmentioning

confidence: 99%

Inattentive Driving Detection Using Body-Worn Sensors: Feasibility Study

Akiduki

Nagasawa

Zhang

et al. 2022

Self Cite

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.

“…Examples are fall detection [23], gait analysis [24], ADL (activity of daily life) [25], etc. The last one is the "how" problem, inferring the body status-related research, such as emotion-sensing [26], respiration/heartrate sensing [27], healthcare [28], etc. This task-oriented categorization aims to supply a basic concept of the objectives of human activity recognition.…”

Section: Object Of Human Activities Recognition (Har)mentioning

confidence: 99%

The State-of-the-Art Sensing Techniques in Human Activity Recognition: A Survey

Bian

Liu

Zhou

et al. 2022

Human activity recognition (HAR) has become an intensive research topic in the past decade because of the pervasive user scenarios and the overwhelming development of advanced algorithms and novel sensing approaches. Previous HAR-related sensing surveys were primarily focused on either a specific branch such as wearable sensing and video-based sensing or a full-stack presentation of both sensing and data processing techniques, resulting in weak focus on HAR-related sensing techniques. This work tries to present a thorough, in-depth survey on the state-of-the-art sensing modalities in HAR tasks to supply a solid understanding of the variant sensing principles for younger researchers of the community. First, we categorized the HAR-related sensing modalities into five classes: mechanical kinematic sensing, field-based sensing, wave-based sensing, physiological sensing, and hybrid/others. Specific sensing modalities are then presented in each category, and a thorough description of the sensing tricks and the latest related works were given. We also discussed the strengths and weaknesses of each modality across the categorization so that newcomers could have a better overview of the characteristics of each sensing modality for HAR tasks and choose the proper approaches for their specific application. Finally, we summarized the presented sensing techniques with a comparison concerning selected performance metrics and proposed a few outlooks on the future sensing techniques used for HAR tasks.