Approaching vehicle detection method with acoustic analysis using smartphone for elderly bicycle driver

Kawanaka, Shogo; Kashimoto, Yukitoshi; Firouzian, Aryan; Arakawa, Yutaka; Pulli, Petri; Yasumoto, Keiichi

doi:10.23919/icmu.2017.8330069

Cited by 9 publications

(3 citation statements)

References 2 publications

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“…Figs. [5][6][7][8] show that some of the 24 features play important roles in discriminating fall events while the other features do not. In this article, to reduce computational complexity, we chose the most crucial 6 features (or components) out of the 24 features using PCA explained in Section III-B.…”

Section: B Pca-based Feature Reductionmentioning

confidence: 99%

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Accident Detection System for Bicycle Riders

2021

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Bicycle riders are exposed to accident injuries such as head trauma. The risk of these riders' injuries is higher compared to the risk of injuries for motorists. Crashes, riders' errors, and environmental hazards are the cause of bicycle-related accidents. In 2017, nearly 50% of bicycle-related accidents occurred in urban areas at night, which may contribute to a delay in reporting the accidents to emergency centers. Hence, a system that can detect the accident is needed to notify urgent care clinics promptly. In this article, we propose a bicycle accident detection system. We designed hardware modules measuring the features related to the riding status of a bicycle and fall accidents. For this purpose, we used a magnetic, angular rate, and gravity (MARG) sensor-based system which measures four different types of signals: 1) acceleration, 2) angular velocity, 3) angle, and 4) magnitude of the riding status. Each of these signals is measured in three different directions (X, Y, and Z). We used two different time-domain parameters, i.e., average and standard deviation. As a result, we considered 24 features. We used principal component analysis (PCA) for feature reduction and the support vector machines (SVM) algorithm for the detection of fall accidents. Experimental results show that our proposed system detects fall accidents during cycling status with 95.2% accuracy, which demonstrates the feasibility of our proposed bicycle accident detection system. Index Terms-Bicycle accident, fall detection, MARG, principal component analysis, support vector machines. I. INTRODUCTION A CCORDING to the Centers for Disease Control and Prevention's (CDC) report published in 2017 [1], bicycle-related accidents lead to around 1,000 fatal and 467,000 non-fatal injuries. There are various causes associated with these bicycle-related accidents such as rider's carelessness, environmental hazards, and crashes with motor vehicles or other bicycles [2]. More than 45% of bicycle-related accidents are reported to occur in dark conditions and urban areas [1], [3]. Moreover, in some situations, the rider may be alone or unable to ask for help due to shock, injuries, or unconsciousness, which can delay medical treatment [4]. There have been studies on analyzing and monitoring riding conditions to increase the safety of bicycle riders [3], [5]-[10]. In [9], a smartphone is used to recognize four different types of riding status-right turn, left turn, straight run, and stop. Here, the smartphone was attached to a bicycle handlebar and collected accelerometer and gyroscope data to recognize the movement type of the bicycle based on Manuscript

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Section: B Pca-based Feature Reductionmentioning

confidence: 99%

“…Collisions with bicycle riders or pedestrians during car driving were simulated in [3]. A collision avoidance method for bicycle riders, which detected approaching vehicles using a smartphone, was proposed in [6]. A vehicle to vehicle (V2V) network-based bicycle rider detection method for car drivers, which provided driver assistance, was proposed in [7].…”

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confidence: 99%

Accident Detection System for Bicycle Riders

2021

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“…In Reference [57], the cycling activity was recognized with Weka (REPTree), reporting an accuracy of 97.4% with frequency spectrum as a feature.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile Devices

et al. 2019

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The identification of Activities of Daily Living (ADL) is intrinsic with the user’s environment recognition. This detection can be executed through standard sensors present in every-day mobile devices. On the one hand, the main proposal is to recognize users’ environment and standing activities. On the other hand, these features are included in a framework for the ADL and environment identification. Therefore, this paper is divided into two parts—firstly, acoustic sensors are used for the collection of data towards the recognition of the environment and, secondly, the information of the environment recognized is fused with the information gathered by motion and magnetic sensors. The environment and ADL recognition are performed by pattern recognition techniques that aim for the development of a system, including data collection, processing, fusion and classification procedures. These classification techniques include distinctive types of Artificial Neural Networks (ANN), analyzing various implementations of ANN and choosing the most suitable for further inclusion in the following different stages of the developed system. The results present 85.89% accuracy using Deep Neural Networks (DNN) with normalized data for the ADL recognition and 86.50% accuracy using Feedforward Neural Networks (FNN) with non-normalized data for environment recognition. Furthermore, the tests conducted present 100% accuracy for standing activities recognition using DNN with normalized data, which is the most suited for the intended purpose.

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