Evaluating Wearable Activity Recognition and Fall Detection Systems

Álvarez-García, Juan Antonio; Morillo, Luis Miguel Soria; Concepción, Miguel Ángel Álvarez de la; Fernández-Montes, Alejandro; Ramírez, Juan Antonio Ortega

doi:10.1007/978-3-319-11128-5_163

Cited by 8 publications

(7 citation statements)

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“…The results revealed that the decision-tree based method performed best with 84% accuracy. Similar efforts have been reported to detect human motions using motion tracking (see [3], [14], [16], [21], [22]). …”

Section: Related Workmentioning

confidence: 62%

“…The availability of inexpensive miniature microelectromechanical system (MEMS) sensors have motivated researchers to study their potential for assessing fallrisk (see [18], [29], [33], and the references therein) as well as monitoring activities of daily livings (ADLs) [3], [8], [11], [12], [20]- [22], [29] and fall events (FEs) [5]- [7], [12]- [15], Manuscript [19], [22], [24]- [26], [28], [30], [31], [33]- [35]. These studies have used accelerometers, gyroscopes, magnetometers, or a combination of them to collect motion datasets at 25 Hz to 200 Hz.…”

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confidence: 99%

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Semi-Automatic Extraction of Training Examples From Sensor Readings for Fall Detection and Posture Monitoring

et al. 2016

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While inexpensive wearable motion-sensing devices have shown great promise for fall detection and posture monitoring, two major problems still exist and have to be solved: 1) a framework for the development of firmware and 2) software to make intelligent decisions. We address both the problems. We propose a generic framework for developing the firmware. We also demonstrate that the k-means clustering algorithm can semi-automatically extract training examples from motion data. Moreover, we trained and evaluated several one-and two-level classification networks to monitor non-fall activities and to detect fall events. The proposed classification networks are the combinations of neural networks and softmax regression. These networks are trained offline with examples extracted by our proposed method. The cross-validation of trained two-level networks shows 100% accuracy for non-fall activities and fall events. The data sets for training and testing have been collected using the devices we assembled with four off-the-shelf components. We have programmed them using a prototype of our proposed framework. The data sets include seven types of nonfall activities and four types of fall events. This paper advances the state of the art for the development and training of wearable devices for monitoring non-fall activities and detecting fall events.Index Terms-Fall detection, learning algorithms, neural networks, MEMS, 9-axis sensor, semi-automatic extraction of training examples, softmax regression.

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Section: Related Workmentioning

confidence: 62%

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confidence: 99%

Semi-Automatic Extraction of Training Examples From Sensor Readings for Fall Detection and Posture Monitoring

et al. 2016

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“…Furthermore, the low adoption barrier on healthcare applications [28] through application markets such as Google Play or AppStore makes them the best option to target the mass market. Some of them are focused on fall detection [29,30], but normally do not cover both ADL and falls [31], so a classification system must be designed to consider them.…”

Section: Activity Recognition Systems For Eldersmentioning

confidence: 99%

Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

Concepción

Morillo

Álvarez-García

et al. 2017

Pervasive and Mobile Computing

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a b s t r a c tCurrently, the lifestyle of elderly people is regularly monitored in order to establish guidelines for rehabilitation processes or ensure the welfare of this segment of the population. In this sense, activity recognition is essential to detect an objective set of behaviors throughout the day. This paper describes an accurate, comfortable and efficient system, which monitors the physical activity carried out by the user. An extension to an awarded activity recognition system that participated in the EvAAL 2012 and EvAAL 2013 competitions is presented. This approach uses data retrieved from accelerometer sensors to generate discrete variables and it is tested in a non-controlled environment. In order to achieve the goal, the core of the algorithm Ameva is used to develop an innovative selection, discretization and classification technique for activity recognition. Moreover, with the purpose of reducing the cost and increasing user acceptance and usability, the entire system uses only a smartphone to recover all the information required.

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“…In [7] an AAL virtual simulator is developed build on the top of Modular Robots Open Simulation Engine (MORSE 3 ). The authors recreate a virtual home automation environment where a handicapped user moves using a wheelchair.…”

Section: Aal Simulatorsmentioning

confidence: 99%

Creating Virtual Humans with Game Engines for Evaluate Ambient Assisted Living Scenarios

Palacios

Álvarez-García

Soria

et al. 2016

Ambient Intelligence- Software and Applications – 7th International Symposium on Ambient Intelligence (ISAmI 2016)

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In order to evaluate AAL systems, virtual environments help to reduce costs and time, but these environments do not include hyper-realistic human movements. This is something crucial to evaluate activity recognition systems. The present work in progress describes how it is looking for a way to solve this problem and its development using a virtual environment. By means of a game engine and adjusting their parameters, simulations of real acceleration data sets have been generated. It is continuing looking for a valid model to follow.

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Evaluating Wearable Activity Recognition and Fall Detection Systems

Cited by 8 publications

References 10 publications

Semi-Automatic Extraction of Training Examples From Sensor Readings for Fall Detection and Posture Monitoring

Semi-Automatic Extraction of Training Examples From Sensor Readings for Fall Detection and Posture Monitoring

Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

Creating Virtual Humans with Game Engines for Evaluate Ambient Assisted Living Scenarios

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