A User-Independent and Sensor-Tolerant Wearable Activity Classifier (original) (raw)

Activity Classification Using Realistic Data From Wearable Sensors

IEEE Transactions on Information Technology in Biomedicine, 2006

Automatic classification of everyday activities can be used for promotion of health-enhancing physical activities and a healthier lifestyle. In this paper, methods used for classification of everyday activities like walking, running, and cycling are described. The aim of the study was to find out how to recognize activities, which sensors are useful and what kind of signal processing and classification is required. A large and realistic data library of sensor data was collected. Sixteen test persons took part in the data collection, resulting in approximately 31 h of annotated, 35-channel data recorded in an everyday environment. The test persons carried a set of wearable sensors while performing several activities during the 2-h measurement session. Classification results of three classifiers are shown: custom decision tree, automatically generated decision tree, and artificial neural network. The classification accuracies using leave-one-subject-out cross validation range from 58 to 97% for custom decision tree classifier, from 56 to 97% for automatically generated decision tree, and from 22 to 96% for artificial neural network. Total classification accuracy is 82% for custom decision tree classifier, 86% for automatically generated decision tree, and 82% for artificial neural network

Toward Practical, In-The-Wild, and Reusable Wearable Activity Classification

2018

Wearable activity classifiers, so far, have been able to perform well with simple activities, strictly-scripted activities, and application-specific activities. In addition, current classification systems suffer from using impractical tight-fitting sensor networks, or only use one loose-fitting sensor node that cannot capture much movement information (e.g., smartphone sensors and wrist-worn sensors). These classifiers either do not address the bigger picture of making activity recognition more practical and being able to recognize more complex and naturalistic activities, or try to address this issue but still perform poorly on many fronts. This dissertation works toward having practical, in-the-wild, and reusable wearable activity classifiers by taking several steps that include the four following main contributions. The dissertation starts by quantifying users' needs and expectations from wearable activity classifiers to set a framework for designing ideal wearable activity classifiers. Data collected from user studies and interviews is gathered and analyzed, then several conclusions are made to set a framework of essential characteristics that ideal wearable activity classification systems should have. Afterward, this dissertation introduces a group of datasets that can be used to benchmark different types of activity classifiers and can accommodate a variety of goals. These datasets help to compare different algorithms in activity classification to assess their performance under various circumstances and with different types of activities. The third main contribution consists of developing a technique that can classify complex activities with wide variations. Testing this technique shows that it is able to accurately classify eight complex daily-life activities with wide variations at an accuracy rate of 93.33%, significantly outperforming the state-of-the-art. This technique is a step forward toward classifying real-life natural activities performed in an environment that allows for wide variations within the activity. Finally, this dissertation introduces a method that can be used on top of any activity classifier that allows access to its matching scores in order to improve its classification accuracy. Testing this method shows that it improves classification results by 11.86% and outperforms the state-of-the-art, therefore taking a step forward toward having reusable activity classification techniques that can be used across users, sensor domains, garments, and applications.

Wearable Computing: Accelerometers' Data Classification of Body Postures and Movements

Proceedings of 21st Brazilian Symposium on Artificial Intelligence. Advances in Artificial Intelligence - SBIA 2012. In: Lecture Notes in Computer Science. , pp. 52-61. Curitiba, PR: Springer Berlin / Heidelberg, 2012. ISBN 978-3-642-34458-9. DOI: 10.1007/978-3-642-34459-6_6., 2012

During the last 5 years, research on Human Activity Recognition (HAR) has reported on systems showing good overall recognition performance. As a consequence, HAR has been considered as a potential technology for e-health systems. Here, we propose a machine learning based HAR classifier. We also provide a full experimental description that contains the HAR wearable devices setup and a public domain dataset comprising 165,633 samples. We consider 5 activity classes, gathered from 4 subjects wearing accelerometers mounted on their waist, left thigh, right arm, and right ankle. As basic input features to our classifier we use 12 attributes derived from a time window of 150ms. Finally, the classifier uses a committee AdaBoost that combines ten Decision Trees. The observed classifier accuracy is 99.4%. DATASET AVAILABLE AT: http://groupware.les.inf.puc-rio.br/har

An Adaptive Embedded System for Physical Activity Recognition

Basic human activity recognition performed by ubiquitous devices represents one important area of research seeking for systems that are simple to use, reliable, accurate, and of low cost. Each human possesses individual and distinct characteristics in the way that performs physical activities such as walking or running. Therefore, it is important that the activity recognition system can adapt to the person through a mechanism of learning. This paper describes a low cost and adaptable embedded device system for human movement classification using machine learning. Three machine learning schemes were tested to detect five different types of human physical activities: lying down, standing, walking, running and falling, using a training set with 413 instances. The best learning scheme, LogitBoost, obtained 98.8% of average true positive accuracy, performing also very well in the fall detection task. The other two learning schemes, Multilayer perceptron and Simple Logistic, obtained aver...

Recognizing Physical Activities using Wearable Devices

IGI Global eBooks, 2017

Physical activity is a major part of the user's context for wearable computing applications. The System should be able to acquire the user's physical activities using body worn sensors. The authors propose developing a personal activity recognition system that is practical, reliable, and can be used for healthcare related applications. They propose to use the wearable device which is a readymade, light weight, small and easy to use device for identifying physical activities (i.e. lying, sitting, walking, standing, cycling, running, ascending stairs and descending stairs), fitness studio activities (i.e. using elliptical trainer, butterfly, bench-press and pull down) and swimming techniques (i.e., dolphin, backstroke , breast-stroke and free-style) using machine learning algorithms. In this chapter, the authors present an approach to build a system that exhibits this property and provides evidence based on user studies. Their results indicate that the system has a good accuracy rate.

Classification Accuracies of Physical Activities Using Smartphone Motion Sensors

Journal of Medical Internet Research, 2012

Background: Over the past few years, the world has witnessed an unprecedented growth in smartphone use. With sensors such as accelerometers and gyroscopes on board, smartphones have the potential to enhance our understanding of health behavior, in particular physical activity or the lack thereof. However, reliable and valid activity measurement using only a smartphone in situ has not been realized. Objective: To examine the validity of the iPod Touch (Apple, Inc.) and particularly to understand the value of using gyroscopes for classifying types of physical activity, with the goal of creating a measurement and feedback system that easily integrates into individuals' daily living. Methods: We collected accelerometer and gyroscope data for 16 participants on 13 activities with an iPod Touch, a device that has essentially the same sensors and computing platform as an iPhone. The 13 activities were sitting, walking, jogging, and going upstairs and downstairs at different paces. We extracted time and frequency features, including mean and variance of acceleration and gyroscope on each axis, vector magnitude of acceleration, and fast Fourier transform magnitude for each axis of acceleration. Different classifiers were compared using the Waikato Environment for Knowledge Analysis (WEKA) toolkit, including C4.5 (J48) decision tree, multilayer perception, naive Bayes, logistic, k-nearest neighbor (kNN), and meta-algorithms such as boosting and bagging. The 10-fold cross-validation protocol was used. Results: Overall, the kNN classifier achieved the best accuracies: 52.3%-79.4% for up and down stair walking, 91.7% for jogging, 90.1%-94.1% for walking on a level ground, and 100% for sitting. A 2-second sliding window size with a 1-second overlap worked the best. Adding gyroscope measurements proved to be more beneficial than relying solely on accelerometer readings for all activities (with improvement ranging from 3.1% to 13.4%). Conclusions: Common categories of physical activity and sedentary behavior (walking, jogging, and sitting) can be recognized with high accuracies using both the accelerometer and gyroscope onboard the iPod touch or iPhone. This suggests the potential of developing just-in-time classification and feedback tools on smartphones.

Activity classification using a single wrist-worn accelerometer

2011 5th International Conference on Software, Knowledge Information, Industrial Management and Applications (SKIMA) Proceedings, 2011

Automatic identification of human activity has led to a possibility of providing personalised services in different domains i.e. healthcare, security and sport etc. With advancement in sensor technology, automatic activity recognition can be done in an unobtrusive and non-intrusive way. The placement of the sensor and wearability are ones of vital keys in the successful activity recognition of free space livings. Experiments were carried out to investigate the use of a single wrist-worn accelerometer for automatic activity classification. The performances of two classification algorithms namely Decision Tree C4.5 and Artificial Neural Network were compared using four different sets of features to classify five daily living activities. The result revealed that Decision Tree C4.5 has outperformed Neural Network regardless of the different sets of features used. The best classification result was achieved using the set containing the most popular and accurate features i.e. mean, minimum, energy and sample differences etc. The best accuracy of 94.13% was achieved using only wrist-worn accelerometer showing a possibility of automatic activity classification with no movement constrain, discomfort and stigmatisation caused by the sensor.

Physical Activity Classification Using a Smart Textile

2018

The aim of this study is to develop a human activity classification system based on a wearable intelligent textile and machine learning techniques. Using the Relief-F feature selection algorithm, we identified a set of relevant features collected by the smart textile. Then, the retained features have fed a classifier in order to recognize the underlying activity. In this respect, we test a support vector machine classifier (SVM) and a k-nearest neighbor classifier (KNN). The results show the reliability of the feature selection procedure and indicate that the activities can be recognized with an overall accuracy of more than 96.37 % using the KNN classifier and 95.4 % using the SVM classifier. Since the Hexoskin intelligent textile also allows the collection of physiological data, these experimental results are very promising for practical applications of acquisition of human activities recognition, which will make it possible to study the patient's state of health or to detect ...

On the issue of variability in labels and sensor configurations in activity recognition systems

Two aspects of the design and characterization of activity recognition systems are rarely elaborated in the literature. First, the influence of system performance with variability in sensor placement and orientation is often overlooked. This is important for the deployment of robust activity recognition systems. Second, the influence of labeling variability is also overlooked, especially w.r.t. label boundary jitter and labeling errors. This is important during the development of an activity recognition system as acquiring labels is costly. We argue that there is a need to explicitly address the consequences of such variability in publications, together with the mitigation strategies that are used. Elaborating on this is required to move the state of the art towards real-world applications, such as in industrial wearable assistance applications or pervasive healthcare.

Activity identification using body-mounted sensors—a review of classification techniques

Physiological Measurement, 2009

With the advent of miniaturised sensing technology, which can be body-worn, it is now possible to collect and store data on different aspects of human movement under the conditions of free-living. This technology has the potential to be used in automated activity profiling systems which produce a continuous record of activity patterns over extended periods of time. Such activity profiling systems are dependent on classification algorithms which can effectively interpret body-worn sensor data and identify different activities. This article reviews the different techniques which have been used to classify normal activities and/or identify falls from body-worn sensor data. The review is structured according to the different analytical techniques and illustrates the variety of approaches which have previously been applied in this field. Although significant progress has been made in this important area, there is still significant scope for further work, particularly in the application of advanced classification techniques to problems involving many different activities.