Detection of equestrian falls using smartphone sensors
(2017) BMEM01 20171Department of Biomedical Engineering
- Abstract
- Horseback riding is enjoyed by millions of people worldwide, both as a competitive sport and as recreational activity. Horses have a congenital habit to flee from potential danger and the nature of the sport carries with it a risk of falling from the horse. Equestrians are often riding alone and the additional risk of being unaided after a severe fall accident is prominent. The need of a smartphone application able to sense a fall and automatically contact preselected relatives is identified. An equestrian fall detection algorithm using smartphone sensor data is developed in this master thesis. The work was made from scratch, including data recording of both normal horseback riding activities and simulated fall events. Additional signal... (More)
- Horseback riding is enjoyed by millions of people worldwide, both as a competitive sport and as recreational activity. Horses have a congenital habit to flee from potential danger and the nature of the sport carries with it a risk of falling from the horse. Equestrians are often riding alone and the additional risk of being unaided after a severe fall accident is prominent. The need of a smartphone application able to sense a fall and automatically contact preselected relatives is identified. An equestrian fall detection algorithm using smartphone sensor data is developed in this master thesis. The work was made from scratch, including data recording of both normal horseback riding activities and simulated fall events. Additional signal representations were derived and features calculated. Their contribution of interesting information was evaluated and the most suitable features made the final threshold based fall detection algorithm. The algorithm was implemented as an Android application running on real time sensor data for evaluation. In total, 53 out of 55 simulated falls were detected and no false alarm was obtained during eleven hours of normal riding activity. The algorithm is considered as suitable for its purpose of increasing equestrian safety. (Less)
- Popular Abstract (Swedish)
- Innovation för ökad säkerhet inom ridsporten
Ungefär 500 000 av Sveriges invånare rider regelbundet på olika nivåer. Att rida ensam är inte ovanligt och farhågan att ramla av hästen är alltid närvarande. Snart kan risken att bli ensam utan hjälp efter ett fall minskas drastiskt, med din egen mobiltelefon som enda verktyg.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8910075
- author
- Magnusson, Malin LU
- supervisor
- organization
- alternative title
- Detektion av fall från häst med hjälp av mobiltelefonens sensorer
- course
- BMEM01 20171
- year
- 2017
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Fall detection, Smartphone sensors, Equestrian safety
- language
- English
- additional info
- 2017-07
- id
- 8910075
- date added to LUP
- 2017-06-27 09:21:54
- date last changed
- 2020-01-01 03:38:50
@misc{8910075,
abstract = {{Horseback riding is enjoyed by millions of people worldwide, both as a competitive sport and as recreational activity. Horses have a congenital habit to flee from potential danger and the nature of the sport carries with it a risk of falling from the horse. Equestrians are often riding alone and the additional risk of being unaided after a severe fall accident is prominent. The need of a smartphone application able to sense a fall and automatically contact preselected relatives is identified. An equestrian fall detection algorithm using smartphone sensor data is developed in this master thesis. The work was made from scratch, including data recording of both normal horseback riding activities and simulated fall events. Additional signal representations were derived and features calculated. Their contribution of interesting information was evaluated and the most suitable features made the final threshold based fall detection algorithm. The algorithm was implemented as an Android application running on real time sensor data for evaluation. In total, 53 out of 55 simulated falls were detected and no false alarm was obtained during eleven hours of normal riding activity. The algorithm is considered as suitable for its purpose of increasing equestrian safety.}},
author = {{Magnusson, Malin}},
language = {{eng}},
note = {{Student Paper}},
title = {{Detection of equestrian falls using smartphone sensors}},
year = {{2017}},
}