| 1. |
- Bleser, Gabriele, et al.
(författare)
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Personalized Physical Activity Monitoring Using Wearable Sensors
- 2015
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Ingår i: Smart Health. - Cham : Springer International Publishing. - 9783319162256 - 9783319162263 ; , s. 99-124
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Bokkapitel (refereegranskat)abstract
- It is a well-known fact that exercising helps people improve their overall well-being; both physiological and psychological health. Regular moderate physical activity improves the risk of disease progression, improves the chances for successful rehabilitation, and lowers the levels of stress hormones. Physical fitness can be categorized in cardiovascular fitness, and muscular strength and endurance. A proper balance between aerobic activities and strength exercises are important to maximize the positive effects. This balance is not always easily obtained, so assistance tools are important. Hence, ambient assisted living (AAL) systems that support and motivate balanced training are desirable. This chapter presents methods to provide this, focusing on the methodologies and concepts implemented by the authors in the physical activity monitoring for aging people (PAMAP) platform. The chapter sets the stage for an architecture to provide personalized activity monitoring using a network of wearable sensors, mainly inertial measurement units (IMU). The main focus is then to describe how to do this in a personalizable way: (1) monitoring to provide an estimate of aerobic activities performed, for which a boosting based method to determine activity type, intensity, frequency, and duration is given; (2) supervise and coach strength activities. Here, methodologies are described for obtaining the parameters needed to provide real-time useful feedback to the user about how to exercise safely using the right technique.
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| 2. |
- Reiss, Attila, et al.
(författare)
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A Competitive Approach for Human Activity Recognition on Smartphones
- 2013
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Ingår i: ESANN 2013. - : ESANN. - 9782874190810 ; , s. 455-460
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Konferensbidrag (refereegranskat)abstract
- This paper describes a competitive approach developed for an activity recognition challenge. The competition was defined on a new and publicly available dataset of human activities, recorded with smartphone sensors. This work investigates different feature sets for the activity recognition task of the competition. Moreover, the focus is also on the introduction of a new, confidence-based boosting algorithm called ConfAda- Boost.M1. Results show that the new classification method outperforms commonly used classifiers, such as decision trees or AdaBoost.M1.
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| 3. |
- Reiss, Attila, et al.
(författare)
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A novel confidence-based multiclass boosting algorithm for mobile physical activity monitoring
- 2015
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Ingår i: Personal and Ubiquitous Computing. - : Springer Science and Business Media LLC. - 1617-4909 .- 1617-4917. ; 19:1, s. 105-121
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Tidskriftsartikel (refereegranskat)abstract
- This paper addresses one of the main challenges in physical activity monitoring, as indicated by recent benchmark results: The difficulty of the complex classification problems exceeds the potential of existing classifiers. Therefore, this paper proposes the ConfAdaBoost.M1 algorithm. This algorithm is a variant of the AdaBoost.M1 that incorporates well-established ideas for confidence-based boosting. ConfAdaBoost.M1 is compared to the most commonly used boosting methods using benchmark datasets from the UCI machine learning repository. Moreover, it is evaluated on an activity recognition and an intensity estimation problem, including a large number of physical activities from the recently released PAMAP2 dataset. The presented results indicate that the proposed ConfAdaBoost.M1 algorithm significantly improves the classification performance on most of the evaluated datasets, especially for larger and more complex classification tasks. Finally, two empirical studies are designed and carried out to investigate the feasibility of ConfAdaBoost.M1 for physical activity monitoring applications in mobile systems.
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| 4. |
- Reiss, Attila, et al.
(författare)
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Activity Recognition Using Biomechanical Model Based Pose Estimation
- 2010
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Ingår i: Smart Sensing and Context, 2010. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642169816 - 9783642169823 ; , s. 42-55
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Konferensbidrag (refereegranskat)abstract
- In this paper, a novel activity recognition method based on signal-oriented and model-based features is presented. The model-based features are calculated from shoulder and elbow joint angles and torso orientation, provided by upper-body pose estimation based on a biomechanical body model. The recognition performance of signal-oriented and model-based features is compared within this paper, and the potential of improving recognition accuracy by combining the two approaches is proved: the accuracy increased by 4–6% for certain activities when adding model-based features to the signal-oriented classifier. The presented activity recognition techniques are used for recognizing 9 everyday and fitness activities, and thus can be applied for e.g., fitness applications or ‘in vivo’ monitoring of patients.
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| 5. |
- Reiss, Attila, et al.
(författare)
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Confidence-based multiclass AdaBoost for physical activity monitoring
- 2013
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Ingår i: ISWC '13: Proceedings of the 2013 International Symposium on Wearable Computers. - New York, NY, USA : ACM. - 9781450321273 ; , s. 13-20
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Konferensbidrag (refereegranskat)abstract
- Physical activity monitoring has recently become an important topic in wearable computing, motivated by e.g. healthcare applications. However, new benchmark results show that the difficulty of the complex classification problems exceeds the potential of existing classifiers. Therefore, this paper proposes the ConfAdaBoost.M1 algorithm. The proposed algorithm is a variant of the AdaBoost.M1 that incorporates well established ideas for confidence based boosting. The method is compared to the most commonly used boosting methods using benchmark datasets from the UCI machine learning repository and it is also evaluated on an activity recognition and an intensity estimation problem, including a large number of physical activities from the recently released PAMAP2 dataset. The presented results indicate that the proposed ConfAdaBoost.M1 algorithm significantly improves the classification performance on most of the evaluated datasets, especially for larger and more complex classification tasks.
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| 6. |
- Reiss, Attila, et al.
(författare)
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Towards Robust Activity Recognition for Everyday Life : Methods and Evaluation
- 2013
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Ingår i: 7th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2013. - : IEEE. - 9781479902965 ; , s. 25-32
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Konferensbidrag (refereegranskat)abstract
- The monitoring of physical activities under realistic, everyday life conditions - thus while an individual follows his regular daily routine - is usually neglected or even completely ignored. Therefore, this paper investigates the development and evaluation of robust methods for everyday life scenarios, with focus on the task of aerobic activity recognition. Two important aspects of robustness are investigated: dealing with various (unknown) other activities and subject independency. Methods to handle these issues are proposed and compared, a thorough evaluation simulates usual everyday scenarios of the usage of activity recognition applications. Moreover, a new evaluation technique is introduced (leave-one-other-activity-out) to simulate when an activity recognition system is used while performing a previously unknown activity. Through applying the proposed methods it is possible to design a robust physical activity recognition system with the desired generalization characteristic.
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| 7. |
- Weber, Markus, et al.
(författare)
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Unsupervised model generation for motion monitoring
- 2011
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Ingår i: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC). - : IEEE. - 9781457706523
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Konferensbidrag (refereegranskat)abstract
- This paper addresses two fundamental requirements of full body motion monitoring: (a) the ability to sense the input of the user and (b) the means to interpret the captured input. Appropriate technology in both areas is required for an interactive virtual reality system to provide feedback in a useful and natural way. This paper combines technologies for both areas: It develops a sensor fusion approach for capturing user input based on miniature on-body inertial and magnetic motion sensors. Furthermore, it presents work in progress to automatically generate models for motion patterns from the captured input. The technology is then used and evaluated in the context of a personalized virtual rehabilitation trainer application.
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