No abstract

We have fully implemented the system and evaluated it using real datasets, partly generated by performing activities in a smart home laboratory, and partly acquired during several months of monitoring of the instrumented home of a senior diagnosed with MCI. Experimental results, including comparisons with other activity recognition techniques, show the effectiveness of SmartFABER in terms of recognition rates.

Abstract-According to the World Health Organization, the rate of people aged 60 or more is growing faster than any other age group in almost every country, and this trend is not going to change in a near future. Since senior citizens are at high risk of non communicable diseases requiring long-term care, this trend will challenge the sustainability of the entire health system. Pervasive computing can provide innovative methods and tools for early detecting the onset of health issues. In this paper we propose a novel method relying on medical models, provided by cognitive neuroscience researchers, describing abnormal activity routines that may indicate the onset of early symptoms of mild cognitive impairment. A non-intrusive sensor-based infrastructure acquires low-level data about the interaction of the individual with home appliances and furniture, as well as data from environmental sensors. Based on those data, a novel hybrid statistical-symbolical technique is used to detect first the activities being performed and then the abnormal aspects in carrying out those activities, which are communicated to the medical center. Differently from related works, our method can detect abnormal behaviors at a fine-grained level, thus providing an important tool to support the medical diagnosis. In order to evaluate our method we have developed a prototype of the system and acquired a large dataset of abnormal behaviors carried out in an instrumented smart home. Experimental results show that our technique has a high precision while generating a small number of false positives.

In the recent years, a rapid growth of IoT devices has been observed, which in turn results in a huge amount of data produced from multiple sources towards the most disparate cloud platforms or the Internet in general. In a typical cloud-centric approach, the data produced by these devices is simply transmitted over the Internet, for consumption and/or storage. However, with the exponential growth in data production rates, the available network resources are becoming the actual bottleneck of this huge data flowing. Therefore, several challenges are appearing in the coming years, which are mainly related to data transmission, processing, and storage along the so-called cloud-to-thing continuum. In fact, one of the most critical requirements of several IoT applications is low latency, which often hinders raw data consumption to happen at the opposite endpoint with respect to its production. In the context of IoT data stream analytics, for instance, the detection of anomalies or rare-events is one of the most demanding tasks, as it needs prompt detection to increase its significance. In this respect, Fog and Edge Computing seem to be the correct paradigms to alleviate these stringent demands in terms of latency and bandwidth as, by leveraging on re-configurable IoT gateways and smart devices able to support the distribution of the overall computational task, they envisage to liquefy data processing along the way from the sensing device to a cloud endpoint. In this paper, we will present IRESE, that is a rare-event detection system able to apply unsupervised machine learning techniques on the incoming data, directly on affordable gateways located in the IoT edge. Notwithstanding the proposed approach enjoys the benefits of a fully unsupervised learning approach, such as the ability to learn from unlabeled data, it has been tested against various audio rare-event categories, such as gunshot, glass break, scream, and siren, achieving precision and recall measures above 90% in detecting such events.

Sensor-based activity monitoring systems promise to prolong independent living of frail elderly people, including those affected by cognitive disorders. Different solutions are already available on the market, which use wireless sensors installed in the home to track the daily living routines of the senior. Those systems provide caregivers with statistics about detected activities; some of them may trigger real-time notifications when they identify a risk situation. Long-term monitoring of finegrained behavioral anomalies can be an important tool to support the diagnosis of neurodegenerative diseases. However, current commercial systems can only monitor high-level activity routines. For this reason, in a previous work we devised a novel method to recognize fine-grained abnormal behaviors of elderly people at home based on sensor data. Experiments in the lab showed the effectiveness of that method. In this paper we present our experience about the implementation of the system in the home of an elderly person with diagnosis of mild cognitive impairment. After illustrating the current implementation, we discuss preliminary results and outline research directions. In particular, a preliminary clinician's assessment indicates the potential utility of this system to support the diagnosis, and the benefits that would be gained by extending the system to monitor additional parameters, including neurovegetative aspects and motor behavior. We also discuss directions for addressing the encountered technological issues, for improving our reasoning algorithms with more extensive support of uncertainty, and for 'closing the loop' by making the senior an active part of the system