A survey of user-centred approaches for smart home transfer learning and new user home automation adaptation

Recent smart home applications enhance the quality of people's home experiences by detecting their daily activities and providing them services that make their daily life more comfortable and safe. Human activity recognition is one of the fundamental tasks that a smart home should accomplish. However, there are still several challenges for such recognition in smart homes, with the target home adaptation process being one of the most critical, since new home environments do not have sufficient data to initiate the necessary activity recognition process. The transfer learning approach is considered the solution to this challenge, due to its ability to improve the adaptation process. This paper endeavours to provide a concrete review of user-centred smart homes along with the recent advancements in transfer learning for activity recognition. Furthermore, the paper proposes an integrated, personalised system that is able to create a dataset for target homes using both survey and transfer learning approaches, providing a personalised dataset based on user preferences and feedback

A survey of user-centred approaches for smart home transfer learning and new user home automation adaptation

Recent smart home applications enhance the quality of people's home experiences by detecting their daily activities and providing them services that make their daily life more comfortable and safe. Human activity recognition is one of the fundamental tasks that a smart home should accomplish. However, there are still several challenges for such recognition in smart homes, with the target home adaptation process being one of the most critical, since new home environments do not have sufficient data to initiate the necessary activity recognition process. The transfer learning approach is considered the solution to this challenge, due to its ability to improve the adaptation process. This paper endeavours to provide a concrete review of user-centred smart homes along with the recent advancements in transfer learning for activity recognition. Furthermore, the paper proposes an integrated, personalised system that is able to create a dataset for target homes using both survey and transfer learning approaches, providing a personalised dataset based on user preferences and feedback

Context-Aware Personalized Activity Modeling in Concurrent Environment

Activity recognition, having endemic impact on smart homes, faces one of the biggest challenges in learning a personalized activity model completely by using a generic model especially for parallel and interleaved activities. Furthermore, inhabitant’s mistaken object interaction may entail in another spurious activity at smart homes. Identifying and removing such spurious activities is another challenging task. Knowledge driven techniques used for recognizing activity models are static in nature, lack contextual representation and may not comprehend spurious actions for parallel/interleaved activities. In this paper, a novel approach for completing the personalized model specific to each inhabitant at smart homes using generic model (incomplete) is presented that can recognize the sequential, parallel, and interleaved activities dynamically while removing the spurious activities semantically. A comprehensive set of experiments and results based upon number of correct (true positivity) or incorrect (false negativity) recognition of activities assert effectiveness of presented approach within a smart hom

Activity Recognition using Hierarchical Hidden Markov Models on Streaming Sensor Data

Activity recognition from sensor data deals with various challenges, such as overlapping activities, activity labeling, and activity detection. Although each challenge in the field of recognition has great importance, the most important one refers to online activity recognition. The present study tries to use online hierarchical hidden Markov model to detect an activity on the stream of sensor data which can predict the activity in the environment with any sensor event. The activity recognition samples were labeled by the statistical features such as the duration of activity. The results of our proposed method test on two different datasets of smart homes in the real world showed that one dataset has improved 4% and reached (59%) while the results reached 64.6% for the other data by using the best methods

On-line Human Activity Recognition from Audio and Home Automation Sensors: comparison of sequential and non-sequential models in realistic Smart Homes

International audienceAutomatic human Activity Recognition (AR) is an important process for the provision of context-aware services in smart spaces such as voice-controlled smart homes. In this paper, we present an on-line Activities of Daily Living (ADL) recognition method for automatic identification within homes in which multiple sensors, actuators and automation equipment coexist, including audio sensors. Three sequence-based models are presented and compared: a Hidden Markov Model (HMM), Conditional Random Fields (CRF) and a sequential Markov Logic Network (MLN). These methods have been tested in two real Smart Homes thanks to experiments involving more than 30 participants. Their results were compared to those of three non-sequential models: a Support Vector Machine (SVM), a Random Forest (RF) and a non-sequential MLN. This comparative study shows that CRF gave the best results for on-line activity recognition from non-visual, audio and home automation sensors

Multi-resident activity recognition using multi-label classification in ambient sensing smart homes

Activity recognition in smart home environment using wireless ambient sensing is a well-known problem that is being researched very actively. Rapid development in the sensing technologies has made human activity recognition very important for various fields such as health care, home monitoring, surveillance, etc. In this paper, we describe the use of Classifier Chain method of the Multi-Label Classification approach to tackle the task of multi-resident activity recognition. We evaluate the developed model of Classifier Chain with K-Nearest Neighbor as base classifier on real world ARAS dataset which consists of two smart homes with evaluation metrics such as accuracy, precision and hamming loss. Through results, it can be inferred that Classifier Chain method successfully caters the problem of multi-resident activity recognition taking into consideration underlying label dependencies

Activity recognition in smart homes with self verification of assignments

Activity recognition in smart homes provides valuable benefits in the field of health and elderly care by remote monitoring of patients. In health care, capabilities of both performing the correct recognition and reducing the wrong assignments are of high importance. The novelty of the proposed activity recognition approach lies in being able to assign a category to the incoming activity, while measuring the confidence score of the assigned category that reduces the false positives in the assignments. Multiple sensors deployed at different locations of a smart home are used for activity observations. For multi-class activity classification, we propose a binary solution using support vector machines, which simplifies the problem to correct/incorrect assignments. We obtain the confidence score of each assignment by estimating the activity distribution within each class such that the assignments with low confidence are separated for further investigation by a human operator. The proposed approach is evaluated using a comprehensive performance evaluation metrics. Experimental results obtained from nine publicly available smart home datasets demonstrate a better performance of the proposed approach compared to the state of the art

Context-Aware Personalized Activity Modeling in Concurrent Environment

Activity recognition, having endemic impact on smart homes, faces one of the biggest challenges in learning a personalized activity model completely by using a generic model especially for parallel and interleaved activities. Furthermore, inhabitant’s mistaken object interaction may entail in another spurious activity at smart homes. Identifying and removing such spurious activities is another challenging task. Knowledge driven techniques used for recognizing activity models are static in nature, lack contextual representation and may not comprehend spurious actions for parallel/interleaved activities. In this paper, a novel approach for completing the personalized model specific to each inhabitant at smart homes using generic model (incomplete) is presented that can recognize the sequential, parallel, and interleaved activities dynamically while removing the spurious activities semantically. A comprehensive set of experiments and results based upon number of correct (true positivity) or incorrect (false negativity) recognition of activities assert effectiveness of presented approach within a smart hom