Predicting the type of physical activity from tri-axial smartphone accelerometer data
نویسندگان
چکیده
منابع مشابه
Activity Recognition Using K-nearest Neighbor Algorithm on Smartphone with Tri-axial Accelerometer
Mobile devices are becoming increasingly sophisticated. These devices are inherently sensors for collection and communication of textual and voice signals. In a broader sense, the latest generation of smart cell phones incorporates many diverse and powerful sensors such as GPS (Global Positioning Systems) sensors, vision sensors (i.e., cameras), audio sensors (i.e., microphones), light sensors,...
متن کاملActivity Recognition from Acceleration Data Collected with a Tri-axial Accelerometer
This paper proposes a high accuracy classifier for human activity based on data collected with a single tri-axial accelerometer mounted on the right part of the hip. The accuracy of this classifier is very important for detecting the postures. Therefore we use methods like acceleration magnitude and neural network and compare them to find the best solution.
متن کاملStep Counts Using a Tri-axial Accelerometer during Activity
Physical activity has been associated with health improvements in a number of populations. Step counting is one of the most commonly used measures of physical activity [1]. Due to their small size and light weight, the use of wearable sensors for step counts has been investigated in many studies [2, 3] as they are suitable for home deployment. One of the main issues with step counts as a physic...
متن کاملPosture and Activity Detection Using a Tri-axial Accelerometer
Quantifying activity levels among healthy older adults and patients can provide objective information on sedentary behavior and physical function [1]. In order to accurately quantify activity in the free-living environment, a robust method for classifying posture and activity needs to be established. Therefore, the purpose of this study was to validate the identification of static postures and ...
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...
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ژورنال
عنوان ژورنال: Journal of Applied Engineering Science
سال: 2021
ISSN: 1451-4117,1821-3197
DOI: 10.5937/jaes0-27166