SARF: Smart Activity Recognition Framework in Ambient Assisted Living

Zolfaghari, Samaneh; Keyvanpour, Mohammad Reza

doi:10.15439/2016f132

Cited by 28 publications

(17 citation statements)

References 37 publications

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“…"Aging in place" for an elderly person is one key element in ambient assisted living (AAL) technologies [1]. For recognition [2][3][4][5][6][7][8][9][10][11][12][13][14][15] and classification of ADL [16,17] are used various mathematical methods such as Hidden Markov Model (HMM), Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM) [18,6], Artificial Neural Networks (ANN) [11] or adaptive-network-based fuzzy inference system (ANFIS) [19,20]. For detection of ADL in SHC it is possible to use RFID [21], PIR [22], CO 2 [23] sensors or presence sensors, on the basis of which probability models of the people's behavior in SH [24] can be built, respecting the privacy [25] of SHC residents [26].…”

Section: Introductionmentioning

confidence: 99%

Monitoring of the daily living activities in smart home care

Vaňuš

Belesova

Martínek

et al. 2017

Hum. Cent. Comput. Inf. Sci.

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One of the key requirements for technological systems that are used to secure independent housing for seniors in their home environment is monitoring of daily living activities (ADL), their classification, and recognition of routine daily patterns and habits of seniors in Smart Home Care (SHC). To monitor daily living activities, the use of a temperature, CO 2 , humidity sensors, and microphones are described in experiments in this study. The first part of the paper describes the use of CO 2 concentration measurement for detecting and monitoring room´s occupancy in SHC. In second part focuses this paper on the proposal of an implementation of Artificial Neural Network based on the Levenberg-Marquardt algorithm (LMA) for the detection of human presence in a room of SHC with the use of predictive calculation of CO 2 concentrations from obtained measurements of temperature (indoor, outdoor) T i , T o and relative air humidity rH. Based on the long-term monitoring (1 month) of operational and technical functions (unregulated, uncontrolled) in an experimental Smart Home (SH), LMA was trained through the data picked up by the sensors of CO 2 , T and rH with the aim to indirectly predict CO 2 leading to the elimination of CO 2 sensor from the measurement process. Within the realized experiment, input parameters of the neuronal network and the number of neurons for LMA were optimized on the basis of calculated values of Root Mean Squared Error, the correlative coefficient (R) and the length of the measured training time ANN. With the use of the trained network ANN, we realized a strictly controlled short-term (11 h) experiment without the use of CO2 sensor. Experimental results verified high method accuracy (>95%) within the short-term and long-term experiments for learned ANN (1.6.2015ANN (1.6. -30.6.2015. For learned ANN (1.2.2014ANN (1.2. -27.2.2014) was verified worse method accuracy (>60%). The original contribution is a verification of a low-cost method for the detection of human presence in the real operating environment of SHC. In the third part of the paper is described the practical implementation of voice control of operating technical functions by the KNX technology in SHC by means of the in-house developed application HESTIA, intended for both the desktop system version and the mobile version of the Windows 10 operating system for mobile phones. The resultant application can be configured for any building equipped with the KNX bus system. Voice control implementation is an in-house solution, no third-party software is used here. Utilization of the voice communication application in SHC was proven on the experimental basis with the combination of measurement CO 2 for ADL monitoring in SHC.

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Section: Introductionmentioning

confidence: 99%

Monitoring of the daily living activities in smart home care

Vaňuš

Belesova

Martínek

et al. 2017

Hum. Cent. Comput. Inf. Sci.

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“…Technological advances have allowed collecting data of the activities of people mainly from the use of video cameras [14,15], environmental sensors [16], and portable devices [4,17,18]. The latter have the advantages of working properly outdoors and of not being sensitive to occlusion or lighting.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Gait Activities Using Acceleration Data from A Smartphone and A Wearable Device

López-Nava

García-Constantino

Favela

2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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Activity recognition is an important task in many fields, such as ambient intelligence, pervasive healthcare, and surveillance. In particular, the recognition of human gait can be useful to identify the characteristics of the places or physical spaces, such as whether the person is walking on level ground or walking down stairs in which people move. For example, ascending or descending stairs can be a risky activity for older adults because of a possible fall, which can have more severe consequences than if it occurred on a flat surface. While portable and wearable devices have been widely used to detect Activities of Daily Living (ADLs), few research works in the literature have focused on characterizing only actions of human gait. In the present study, a method for recognizing gait activities using acceleration data obtained from a smartphone and a wearable inertial sensor placed on the ankle of people is introduced. The acceleration signals were segmented based on the automatic detection of strides, also called gait cycles. Subsequently, a feature vector of the segmented signals was extracted, which was used to train four classifiers using the Naive Bayes, C4.5, Support Vector Machines, and K-Nearest Neighbors algorithms. Data was collected from seven young subjects who performed five gait activities: (i) going down an incline, (ii) going up an incline, (iii) walking on level ground, (iv) going down stairs, and (v) going up stairs. The results demonstrate the viability of using the proposed method and technologies in ambient assisted living contexts.

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“…In particular, when we want to apply machine learning algorithms for activity pattern recognition in smart home scenario, we follow the sequential occurrence of regular activity in order to find changes in patterns of individual lifestyle. In addition, there are many methods for activity recognition in the related area, therefore it become important to consider general classification and examination of each approach and their implementation constraints for specific problem solution (Zolfaghari and Keyvanpour, 2016). In our research work, we applied the bottom-up approach for human activity recognition based on the data-driven probabilistic model.…”

Section: A Bottom Up Approach: Acm (Ambient Cognition Model) For Inhamentioning

confidence: 99%

A Cognitive-IoE Approach to Ambient-intelligent Smart Home

Jamnal

Liu

2017

Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security

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In today's world, we are living in busy metropolitan cities and want our homes to be ambient intelligent enough towards our cognitive requirements for assisted living in smart space environment and an excellent smart home control system should not rely on the users' instructions. Cognitive IoE is a new state-of-art computing paradigm for interconnecting and controlling network objects in context-aware perception-action cycle for our cognitive needs. The interconnected objects (sensors, RFID, network objects etc.) behave as agents to learn, think and adapt situations according to dynamic contextual environment with no or minimum human intervention. One most important recent research problem is "how to recognize inhabitant activity patterns from the observed sensors data". In this paper, we proposed a two level classification model named as ACM (Ambient Cognition Model) for inhabitant's activities pattern recognition, using Hidden Markov Model based probabilistic model and subtractive clustering classification method. While subtractive clustering separates similar activity states from non-similar activity state, a HMM works as the top layer to train systems for temporal-sequential activities to learn and predict inhabitant activity pattern proactively. The proposed ACM framework play, a significant role to identify user activity intention in more proactive manner such as routine, location, social activity intentions in smart home scenario. The experimental results have been performed on Matlab simulation to evaluate the efficiency and accuracy of proposed ACM model.

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SARF: Smart Activity Recognition Framework in Ambient Assisted Living

Cited by 28 publications

References 37 publications

Monitoring of the daily living activities in smart home care

Monitoring of the daily living activities in smart home care

Recognition of Gait Activities Using Acceleration Data from A Smartphone and A Wearable Device

A Cognitive-IoE Approach to Ambient-intelligent Smart Home

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