Physical activity classification and assessment using smartphone
01 Nov 2018-Vol. 2018, pp 140-144
TL;DR: A smart-phone based system to recognize, classify and evaluate jogging, walking, and standing activities using the GPS and the accelerometer built-in smartphone sensors and notifies caregivers and patients if the level of physical activities of patients falls below a certain threshold.
Abstract: Physical activity is a very important indicator of healthy lifestyle. Physical activity recognition, classification and evaluation is a significant research area, both in academic as well as in healthcare domain to help patients achieving the benefits of performing physical activities. Physical activity recognition and evaluation enable professionals to make appropriate decisions and interventions, and patients can manage their activities of daily living independently. We present in this paper our smart-phone based system to recognize, classify and evaluate jogging, walking, and standing activities using the GPS and the accelerometer built-in smartphone sensors. Our system notifies caregivers and patients if the level of physical activities of patients falls below a certain threshold.
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20 Oct 2021
TL;DR: In this paper, a monitoring system based on a mobile application for phones with an Android operating system and a Firebase backend is proposed, which can help people maintain a balanced life.
Abstract: In the era where all devices are interconnected and information is easily accessed, more and more people are beginning to develop a harmful lifestyle due to sedentarism. People spend more and more time in front of monitors, and thus physical activity is neglected. In this way, the sedentary lifestyle is installed. To help people, a system has been developed for monitoring physical activity using a mobile phone. Today almost all phones have motion sensors and body sensors, so the smartphone will serve as a personal health monitor. This device is indispensable, existing in a lot of variants from different manufacturers. The paper proposes a monitoring system based on a mobile application for phones with an Android operating system and a Firebase backend. This focus will be on the problems caused by a sedentary lifestyle and how the monitoring system can help people maintain a balanced life. Also, more data will be collected from different phones with different versions of Android. A brief analysis will be made that will determine if the application can have good results on both newer and older phone models.
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Book•
15 Oct 1992TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Abstract: From the Publisher:
Classifier systems play a major role in machine learning and knowledge-based systems, and Ross Quinlan's work on ID3 and C4.5 is widely acknowledged to have made some of the most significant contributions to their development. This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use , the source code (about 8,800 lines), and implementation notes. The source code and sample datasets are also available on a 3.5-inch floppy diskette for a Sun workstation.
C4.5 starts with large sets of cases belonging to known classes. The cases, described by any mixture of nominal and numeric properties, are scrutinized for patterns that allow the classes to be reliably discriminated. These patterns are then expressed as models, in the form of decision trees or sets of if-then rules, that can be used to classify new cases, with emphasis on making the models understandable as well as accurate. The system has been applied successfully to tasks involving tens of thousands of cases described by hundreds of properties. The book starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting. Advantages and disadvantages of the C4.5 approach are discussed and illustrated with several case studies.
This book and software should be of interest to developers of classification-based intelligent systems and to students in machine learning and expert systems courses.
21,674 citations
03 Dec 2012
TL;DR: This paper presents a system for human physical Activity Recognition using smartphone inertial sensors and proposes a novel hardware-friendly approach for multiclass classification that adapts the standard Support Vector Machine and exploits fixed-point arithmetic for computational cost reduction.
Abstract: Activity-Based Computing [1] aims to capture the state of the user and its environment by exploiting heterogeneous sensors in order to provide adaptation to exogenous computing resources. When these sensors are attached to the subject's body, they permit continuous monitoring of numerous physiological signals. This has appealing use in healthcare applications, e.g. the exploitation of Ambient Intelligence (AmI) in daily activity monitoring for elderly people. In this paper, we present a system for human physical Activity Recognition (AR) using smartphone inertial sensors. As these mobile phones are limited in terms of energy and computing power, we propose a novel hardware-friendly approach for multiclass classification. This method adapts the standard Support Vector Machine (SVM) and exploits fixed-point arithmetic for computational cost reduction. A comparison with the traditional SVM shows a significant improvement in terms of computational costs while maintaining similar accuracy, which can contribute to develop more sustainable systems for AmI.
802 citations
"Physical activity classification an..." refers methods in this paper
...Mitchell [14] used smartphones worn on the back to collect sport data, and in [15] smartphone was attached to the waist to collect data of daily activities....
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01 Jan 1996
TL;DR: A range of machine learning techniques are presented to the user in such a way as to hide the idiosyncrasies of input and output formats, as to allow an exploratory approach in applying the technology.
Abstract: WEKA is a workbench designed to aid in the application of machine learning technology to real world data sets, in particular, data sets from New Zealand’s agricultural sector In order to do this a range of machine learning techniques are presented to the user in such a way as to hide the idiosyncrasies of input and output formats, as well as allow an exploratory approach in applying the technology The system presented is a component based one that also has application in machine learning research and education
532 citations
"Physical activity classification an..." refers methods in this paper
...Weka J48 decision tree algorithm is selected because it gives results in a hierarchical decision tree model that can be easily manipulated by applications....
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...J48 is used by many activity recognition research works as an off-line classification approach....
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...Keywords— smart phone; sensors; GPS; accelerometer; physical activity recognition; physical activity assessment; walking; jogging; standing; machine learning; J48....
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...Martin et al. [10] revealed that J48 outperforms both decision tables and naive Bayes classifiers....
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...According to a study performed by Lombriser et al [11], KNN and J48/C4.5 are identified as the classifiers with the least complexity however they render acceptable performance....
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TL;DR: This paper presents a comprehensive survey of the recent advances in activity recognition with smartphones' sensors, starting with the basic concepts such as sensors, activity types, etc and reviewing the core data mining techniques behind the main stream activity recognition algorithms.
335 citations
"Physical activity classification an..." refers background in this paper
...[8] surveyed numerous existing approaches for human physical activity recognition based on the built-in sensors of a smartphone....
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TL;DR: A smartphone-based fall detection system that monitors the movements of patients, recognizes a fall, and automatically sends a request for help to the caregivers, to reduce the problem of false alarms.
320 citations
"Physical activity classification an..." refers background in this paper
...Smartphones are held by all people nowadays and they allow the system to operate in an outdoor environment, even in faraway regions; also, communications rely on the existing cellular networks [5]....
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