Classification of Sporting Activities Using Smartphone Accelerometers
نویسندگان
چکیده
In this paper we present a framework that allows for the automatic identification of sporting activities using commonly available smartphones. We extract discriminative informational features from smartphone accelerometers using the Discrete Wavelet Transform (DWT). Despite the poor quality of their accelerometers, smartphones were used as capture devices due to their prevalence in today's society. Successful classification on this basis potentially makes the technology accessible to both elite and non-elite athletes. Extracted features are used to train different categories of classifiers. No one classifier family has a reportable direct advantage in activity classification problems to date; thus we examine classifiers from each of the most widely used classifier families. We investigate three classification approaches; a commonly used SVM-based approach, an optimized classification model and a fusion of classifiers. We also investigate the effect of changing several of the DWT input parameters, including mother wavelets, window lengths and DWT decomposition levels. During the course of this work we created a challenging sports activity analysis dataset, comprised of soccer and field-hockey activities. The average maximum F-measure accuracy of 87% was achieved using a fusion of classifiers, which was 6% better than a single classifier model and 23% better than a standard SVM approach.
منابع مشابه
Improvement of the Effective Components in the PDR Positioning Method Based on Detecting the User’s Movement Mode Using Smartphone Sensors
The purpose of this paper is to evaluate and improve the accuracy of indoor positioning using smartphone sensors based on Pedestrian Dead Reckoning (PDR) method. In some specific situations, such as fires or power outages that disable infrastructure-based positioning techniques, using PDR method based on smartphone sensors that perform positioning continuously is a good solution.This paper focu...
متن کاملActivity Classification using Smartphone Accelerometer Data
There have been some research efforts into identifying activities from smartphone accelerometer data. Kwapisz et al. [1] mined data from smartphone sensors of different users doing different activities, and extracted statistics like the average, standard deviation, and time between peaks from portions of the data. With the features they used, they achieved 91.7% precision overall using Multilay...
متن کاملUsing Smartphone - based Accelerometer to Detect Travel by Metro Train by Megha
We look at the problem of using accelerometer in smartphones to detect whether the user is at a metro train station or in a metro train. Currently, we have solutions to detect simple activities, such as sitting or walking. Our work for this thesis investigates the more complex problem of discerning “in-train” from “in-metro-station” activities which internally are composed of several simple act...
متن کاملHuman Activity Recognition Using Smartphone and Smartwatch
Human activity recognition is influential subject in different fields of human daily life especially in the mobile health. As the smartphone becomes an integrated part of human daily life which has the ability of complex computation, internet connection and also contains a large number of hardware sensors, encourage implementation of the human activity recognition system. Most of the works done...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره 13 شماره
صفحات -
تاریخ انتشار 2013