Identifying Different Motions Using Statistical Methods
(2019) In Bachelor's Theses in Mathematical Sciences MASK01 20182Mathematical Statistics
- Abstract
- This bachelor thesis aims to explore how well one can classify different types of motions using only data gathered from a mobile phones
gyroscope and accelerometer. The methods includes extracting features from the covariance estimations of each signal and taking timefrequency transforms of the data and classify the transforms with a
convolutional neural network. The results are positive and shows there
are ways to design algorithms that classify the motions correctly. - Popular Abstract
- To many of us, the mobile phone has become an integrated part of our life. It helps us keep contact with friends but it also helps us during our every day life with various tasks, such as a calendar. While I did not construct an app, I explored to possibilities let the mobile phone aid us in an exercise session. With the help of the gyroscope and accelerometer - which keeps track of the phones rotation and acceleration respectively - to together keep track of how you move around, and use that information to track how many repeats of each motion you do.
The data that has been used in this study comes from 5 individuals, including myself, who in total produced 12 datasets. For each dataset, the individual performing the motions got the... (More) - To many of us, the mobile phone has become an integrated part of our life. It helps us keep contact with friends but it also helps us during our every day life with various tasks, such as a calendar. While I did not construct an app, I explored to possibilities let the mobile phone aid us in an exercise session. With the help of the gyroscope and accelerometer - which keeps track of the phones rotation and acceleration respectively - to together keep track of how you move around, and use that information to track how many repeats of each motion you do.
The data that has been used in this study comes from 5 individuals, including myself, who in total produced 12 datasets. For each dataset, the individual performing the motions got the same phone strapped to their upper arm, and was told how many repeats of what motion to do. For example, one could be told to do 10 push-ups, take a small break, then do 10 more push-ups, then finish.
Two different approaches when analyzing the data was done, one approach consisted of exploiting statistical properties of the data, and the other approach included time-frequency analysis and neural networks.
From the results, one can conclude that it definitely is possible to classify these motions well with both the approaches, and thus it feels plausible that an app to keep track of your training can be constructed. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8996900
- author
- Liu, Owen LU
- supervisor
- organization
- course
- MASK01 20182
- year
- 2019
- type
- M2 - Bachelor Degree
- subject
- publication/series
- Bachelor's Theses in Mathematical Sciences
- report number
- LUNFMS-4039-2019
- ISSN
- 1654-6229
- other publication id
- 2019:K27
- language
- English
- id
- 8996900
- date added to LUP
- 2019-11-26 13:07:08
- date last changed
- 2019-11-26 13:07:08
@misc{8996900, abstract = {{This bachelor thesis aims to explore how well one can classify different types of motions using only data gathered from a mobile phones gyroscope and accelerometer. The methods includes extracting features from the covariance estimations of each signal and taking timefrequency transforms of the data and classify the transforms with a convolutional neural network. The results are positive and shows there are ways to design algorithms that classify the motions correctly.}}, author = {{Liu, Owen}}, issn = {{1654-6229}}, language = {{eng}}, note = {{Student Paper}}, series = {{Bachelor's Theses in Mathematical Sciences}}, title = {{Identifying Different Motions Using Statistical Methods}}, year = {{2019}}, }