Experimental examination and simulation analysis of standing-type personal mobility device sharing

2016 ◽  
Author(s):  
Kohji Tomita ◽  
Naohisa Hashimoto ◽  
Akiya Kamimura ◽  
Masashi Yokozuka ◽  
Osamu Matsumoto
Keyword(s):  
Simulation Analysis ◽  
Experimental Examination ◽  
Personal Mobility ◽  
Mobility Device

scholarly journals Patterns of orthopaedic injury among hospitalised personal mobility device users and bicycle riders: a comparative study

2021 ◽  
Author(s):  
TSD Koh ◽  
YL Woo ◽  
TH Wong ◽  
MH Tan
Keyword(s):  
Orthopaedic Surgery ◽  
Surgical Intervention ◽  
High Energy ◽  
Open Fractures ◽  
Inpatient Stay ◽  
Injury Patterns ◽  
Prolonged Hospitalisation ◽  
Personal Mobility ◽  
Mobility Devices ◽  
Mobility Device

Introduction: Personal mobility devices (PMDs), such as electronic scooters or motorised bicycles, are efficient modes of transportation. Their recent popularity has also resulted in an increase in PMD-related injuries. We aimed to characterise and compare the nature of injuries sustained by PMD users and bicycle riders. Methods: This retrospective study compared injury patterns among PMD and bicycle users. 140 patients were admitted between November 2013 and September 2018. Parameters studied included patients’ demographics (e.g. age, gender and body mass index), type of PMD, nature of injury, surgical intervention required, duration of hospitalisation and time off work. Results: Of 140 patients, 46 (32.9%) patients required treatment at the department of orthopaedic surgery. 19 patients were PMD users while 27 were bicycle riders. 16 (84.2%) patients with PMD-related injuries were men. PMD users were significantly younger (mean age 45 ± 15 years) when compared to bicycle riders (mean age 56 ±17 years; p < 0.05). A quarter (n = 5, 26.3%) of PMD users sustained open fractures and over half (n = 10, 52.6%) required surgical intervention. Among 27 bicycle users, 7.4% (n = 2) of patients sustained open fractures and 70.4% (n = 19) required surgical intervention. Both groups had comparable inpatient stay duration and time off work. Conclusion: PMD-related orthopaedic traumas are high-energy injuries, with higher rates of open fractures, when compared to bicycle injuries. In addition, PMD users are significantly younger and of economically viable age. Prolonged hospitalisation and time off work have socioeconomic implications. Caution should be exercised when using PMDs.

scholarly journals Effects of Human Factors on Public Use of Standing-Type Personal Mobility Vehicle

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Naohisa Hashimoto ◽  
Kohji Tomita ◽  
Osamu Matsumoto ◽  
Ali Boyali
Keyword(s):  
Future Development ◽  
Real World ◽  
Public Transport ◽  
Urban Areas ◽  
Traffic Accidents ◽  
Transport Systems ◽  
Personal Mobility ◽  
Mobility Devices ◽  
Personal Mobility Vehicle ◽  
Mobility Device

To significantly reduce the occurrence of severe traffic accidents, reducing the number of vehicles in urban areas should be considered. Personal mobility is essential for realizing this reduction, which requires consideration of the last-/first-mile problem. The overall objective of our research is to solve this problem using standing-type personal mobility vehicles as transportation devices; however, to evaluate the feasibility of such vehicles as future mobility devices, it is necessary to evaluate their operation under real-world conditions. Therefore, in this study, experimental and survey data relating to the velocity, stability, safety, and comfort of a standing-type personal mobility device are obtained to evaluate its performance in three different scenarios. The results show that the personal mobility vehicle is socially well received and can be safely operated on sidewalks, irrespective of the gender or age of the driver; moreover, the results suggest that subjects who routinely use a bicycle are adept at avoiding and absorbing the impacts of small holes and bumps, thereby yielding reduced acceleration values (in all directions) and pitch, roll, and yaw rates. This is anticipated to benefit the future development of personal mobility devices and help realize effective and accessible public transport systems, as well as reduce the number of vehicles in urban areas.

Design and Implementation of Smart Vest for the Safety of Personal Mobility Device Users

2020 ◽  
Vol 10 (1) ◽  
pp. 85-97
Author(s):  
Sungjun Han ◽  
Younghoon Kim ◽  
Haebin Park ◽  
Woosung Choi ◽  
Eunju Park ◽  
...  
Keyword(s):  
Personal Mobility ◽  
Design And Implementation ◽  
Mobility Device

Electric scooter burns and the danger of personal mobility device battery

2018 ◽  
Vol 88 (3) ◽  
pp. 250-250 ◽  
Author(s):  
Shao Nan Khor ◽  
Si Jack Chong ◽  
Kok Chai Tan
Keyword(s):  
Personal Mobility ◽  
Electric Scooter ◽  
Mobility Device

scholarly journals Impact of the face angle to traveling trajectory during the riding standing-type personal mobility device

2018 ◽  
Vol 161 ◽  
pp. 03001
Author(s):  
Jeyeon Kim ◽  
Kenta Sato ◽  
Naohisa Hashimoto ◽  
Alexey Kashevnik ◽  
Kohji Tomita ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Developed Countries ◽  
Personal Mobility ◽  
Recreational Activities ◽  
The Face ◽  
The Usa ◽  
The Developed Countries ◽  
Mobility Device ◽  
The Impact

In this paper, we investigate the impact of face direction during traveling by Standing-Type Personal Mobility Device (PMD). The use of PMD devices has been a popular choice for recreational activities in the developed countries such as in the USA and the countries in Europe. These devices are not completely risk free and various accidents have been reported. Since that, the risk factors leading to accidents have to be investigated. Unfortunately, the research studies on the risk factors on riding PMD devices have not been matured as much as the studies on driving cars. In this paper, we evaluate the impacts of face angle on travelling trajectory during travelling in a PMD. We showed by experiments that, the face direction is an important factor in risk assessment for traveling by a PMD.

scholarly journals Design of a Personal Mobility Device for Elderly Users

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Natthanon Phannil ◽  
Chaiyan Jettanasen
Keyword(s):  
Air Quality ◽  
Detection System ◽  
The Elderly ◽  
Electrical Equipment ◽  
Service Development ◽  
Special Equipment ◽  
Ageing Society ◽  
Personal Mobility ◽  
Electric Equipment ◽  
Mobility Device

The ageing society has resulted in imbalances in the population age ratio. The ratio of working-age people was less than that of elderly people resulting in a shortage of elderly caregivers and increased healthcare costs. Although the lifestyle the elderly remains the same, their physical abilities are reduced, requiring them to rely on special equipment when traveling in order to gain more control and safety. Therefore, the Elderly Personal Mobility Device (EPMD) is developed using Internet of Things (IoT) technology to reduce the burden of caregivers, provide freedom and safety for elderly travelers, assess air pollution risks, and alert the occurrence of emergency events. The EPMD is designed in terms of structure, electrical equipment, and sensor systems. First, the shapes, sizes, and thicknesses of the carbon steel used for construction of the EPMD structure are calculated by using SolidWorks software. Next, the electric equipment is carefully selected to meet the requirements of actual use. Finally, the sensor system is designed to monitor the EPMD status and air quality using IoT devices to create a data interface and big data for elderly health service development, as well as an air quality map with distributed measuring stations and a charging station detection system for future use.

scholarly journals Survey Data Analysis on Intention to Use Shared Mobility Services

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Eunjeong Ko ◽  
Hyungjoo Kim ◽  
Jinwoo Lee
Keyword(s):  
Intention To Use ◽  
Survey Period ◽  
Personal Mobility ◽  
Factors Affecting ◽  
Mobility Devices ◽  
Transportation Modes ◽  
Shared Mobility ◽  
Gyeonggi Province ◽  
Smart Mobility ◽  
Mobility Device

Shared mobility is a service that allows users to share various transportation modes and use them with reservations when necessary. It started with private automotive car-sharing and ride-sharing services. Currently, it operates on a wider range, including personal mobility devices such as electric bicycles and scooters. The purpose of this study is to derive a direction for providing future shared mobility services through analysis of factors affecting the usage intention of both current and prospective users. The survey targets 753 citizens living in Gyeonggi Province, Korea. The survey period is from February 12, 2020, to February 26, 2020. In this study, a logistic regression analysis is conducted to investigate the factors affecting the use intention of shared mobility. The analysis results show that gender, car ownership, and education, among variables reflecting socio-demographic characteristics, have significant effects on intention to use shared mobility. Moreover, we find that experience factors, including mainly used transportation modes, ownership of shared mobility device, past experience in similar services, satisfaction of existing shared mobility services, and distance from the home to the nearest bus stop, are also statistically influential. The analysis results are expected to lay the foundation for the introduction of shared mobility services and can be used as data for planning smart mobility services in the future.

scholarly journals Context-Based Rider Assistant System for Two Wheeled Self-Balancing Vehicles

2019 ◽  
Vol 18 (3) ◽  
pp. 583-614 ◽  
Author(s):  
Jeyeon Kim ◽  
Kenta Sato ◽  
Naohisa Hashimoto ◽  
Alexey Kashevnik ◽  
Kohji Tomita ◽  
...  
Keyword(s):  
Current Situation ◽  
Context Information ◽  
Personal Mobility ◽  
Work Analysis ◽  
Video Cameras ◽  
Dangerous Situation ◽  
Last Mile ◽  
State Determination ◽  
Assistant Systems ◽  
Mobility Device

Personal mobility devises become more and more popular last years. Gyroscooters, two wheeled self-balancing vehicles, wheelchair, bikes, and scooters help people to solve the first and last mile problems in big cities. To help people with navigation and to increase their safety the intelligent rider assistant systems can be utilized that are used the rider personal smartphone to form the context and provide the rider with the recommendations. We understand the context as any information that characterize current situation. So, the context represents the model of current situation. We assume that rider mounts personal smartphone that allows it to track the rider face using the front-facing camera. Modern smartphones allow to track current situation using such sensors as: GPS / GLONASS, accelerometer, gyroscope, magnetometer, microphone, and video cameras. The proposed rider assistant system uses these sensors to capture the context information about the rider and the vehicle and generates context-oriented recommendations. The proposed system is aimed at dangerous situation detection for the rider, we are considering two dangerous situations: drowsiness and distraction. Using the computer vision methods, we determine parameters of the rider face (eyes, nose, mouth, head pith and rotation angles) and based on analysis of this parameters detect the dangerous situations. The paper presents a comprehensive related work analysis in the topic of intelligent driver assistant systems and recommendation generation, an approach to dangerous situation detection and recommendation generation is proposed, and evaluation of the distraction dangerous state determination for personal mobility device riders.

scholarly journals A Study on the Application of LSTM to Judge Bike Accidents for Inflating Wearable Airbags

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6541
Author(s):  
So-Hyeon Jo ◽  
Joo Woo ◽  
Gi-Sig Byun ◽  
Baek-Soon Kwon ◽  
Jae-Hoon Jeong
Keyword(s):  
Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Occurrence Rate ◽  
Series Data ◽  
Measurement Unit ◽  
Judgment Accuracy ◽  
Personal Mobility ◽  
Long Short Term Memory ◽  
Mobility Device

The traffic accident occurrence rate is increasing relative to the increase in the number of people using personal mobility device (PM). This paper proposes an airbag system with a more efficient algorithm to decide the deployment of a wearable bike airbag in case of an accident. The existing wearable airbags are operated by judging the accident situations using the thresholds of sensors. However, in this case, the judgment accuracy can drop against various motions. This study used the long short-term memory (LSTM) model using the sensor values of the inertial measurement unit (IMU) as input values to judge accident occurrences, which obtains data in real time from the three acceleration-axis and three angular velocity-axis sensors on the driver motion states and judges whether or not an accident has occurred using the obtained data. The existing neural network (NN) or convolutional neural network (CNN) model judges only the input data. This study confirmed that this model has a higher judgment accuracy than the existing NN or CNN by giving strong points even in “past information” through LSTM by regarding the driver motion as time-series data.

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