Surveying human habit modeling and mining techniques in smart spaces

A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field

Hierarchical Hidden Markov Model in Detecting Activities of Daily Living in Wearable Videos for Studies of Dementia

International audienceThis paper presents a method for indexing activities of daily living in videos obtained from wearable cameras. In the context of dementia diagnosis by doctors, the videos are recorded at patients' houses and later visualized by the medical practitioners. The videos may last up to two hours, therefore a tool for an efficient navigation in terms of activities of interest is crucial for the doctors. The specific recording mode provides video data which are really difficult, being a single sequence shot where strong motion and sharp lighting changes often appear. Our work introduces an automatic motion based segmentation of the video and a video structuring approach in terms of activities by a hierarchical two-level Hidden Markov Model. We define our description space over motion and visual characteristics of video and audio channels. Experiments on real data obtained from the recording at home of several patients show the difficulty of the task and the promising results of our approach

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-level Attention Model for Weakly Supervised Audio Classification

In this paper, we propose a multi-level attention model to solve the weakly labelled audio classification problem. The objective of audio classification is to predict the presence or absence of audio events in an audio clip. Recently, Google published a large scale weakly labelled dataset called Audio Set, where each audio clip contains only the presence or absence of the audio events, without the onset and offset time of the audio events. Our multi-level attention model is an extension to the previously proposed single-level attention model. It consists of several attention modules applied on intermediate neural network layers. The output of these attention modules are concatenated to a vector followed by a multi-label classifier to make the final prediction of each class. Experiments shown that our model achieves a mean average precision (mAP) of 0.360, outperforms the state-of-the-art single-level attention model of 0.327 and Google baseline of 0.314.Comment: 5 pages, 3 figures, Submitted to Eusipco 201