Activity Discovery and Detection of Behavioral Deviations of an Inhabitant From Binary Sensors

Abstract

The aim of this paper is to improve the autonomy of medically monitored patients in a smart home instrumented only with binary sensors; overwatching the disease evolution, that can be characterized by behaviour changes, is helped by detecting the activities the inhabitant performs. Two contributions are presented. On one hand, using sequence mining methods in the flow of sensor events, the most frequent patterns mirroring activities of the inhabitant are discovered; these activities are then modeled by an extended finite automaton, which can then be used for activity recognition and generate activity events. On the other hand, given the set of activities that can be recognized, another automaton is built to model requirements from the medical staff supervising the inhabitant; it accepts activity events, and residuals are defined to detect any behaviour deviation. The whole method is applied to the dataset of Domus, an instrumented smart home. Note to Practitioners-This paper was motivated by the will of providing a model of the behaviour of the inhabitant at home, from which formal methods can be applied to verify some properties, namely detect behaviour deviations. The input of the framework is a log file of binary sensor events (rising and falling edges); a pre-filtering of the log might be required to get rid of repetitive, noisy events (namely spurious events generated by motion sensors, for which only the first rising edge and last falling edges are relevant). This recorded log is assumed to be recorded without sensor faults, and to be characteristic of a typical routine of the inhabitant. In order to build this model, based on the Extended Finite Automata (EFA) formalism, sequence mining techniques (first contribution of the article) are adapted: the more frequent the sequence is, the more important for the inhabitant the habit is. The second contribution of the paper is the ability to detect behaviour deviations, which is criticial for healthcare at home and disease management. The technique of "residuals", mainly used in manufacturing, has been adapted to EFAs. It requires medical requirements, defined by the medical staff, based on the activities it wants to keep an eye on. A suiting log of sensors events and medical requirements are the only prerequisite to the application of the method.

Situation‐centered goal reinforcement of activities of daily living in smart home environments

“…Smart environments are sensor‐laden settings capable of monitoring, identifying their residents and the activities being performed, as well as reacting to their needs (Synnott, Nugent, & Jeffers, ). A smart home is an intelligent space that is primarily designed to improve the experience and quality of life of its residents via human‐centered applications in order to help adults age‐in‐place, and also to promote independent living by providing prompts on how to complete their intended ADLs at a given time instance (Davis et al, ; Quesada et al, ; Saives, Pianon, & Faraut, ). This technology can provide support to older adults, especially those with cognitive diseases (e.g., dementia) who may not be able to complete an ADL at a given time due to loss of judgment or episode of confusion (Helal et al, ).…”

“…A major study conducted by (Saives et al, ) focused on improving the independence of residents with specific health conditions via continuous monitoring of disease progression as characterized by changes in lifestyle via the recognition of activities performed by the resident. In order to achieve residents' goals, the researchers used a two‐pronged approach.…”

“…experience and quality of life of its residents via human-centered applications in order to help adults age-in-place, and also to promote independent living by providing prompts on how to complete their intended ADLs at a given time instance (Davis et al, 2017;Quesada et al, 2015;Saives, Pianon, & Faraut, 2015). This technology can provide support to older adults, especially those with cognitive diseases (e.g., dementia) who may not be able to complete an ADL at a given time due to loss of judgment or episode of confusion (Helal et al, 2003).…”

“…This technology can provide support to older adults, especially those with cognitive diseases (e.g., dementia) who may not be able to complete an ADL at a given time due to loss of judgment or episode of confusion (Helal et al, 2003). Smart homes possess the capability for human activity recognition (HAR) and detection of resident the goals via observation of actions performed and other environmental conditions captured by relevant sensor values or signals (Quesada et al, 2015) A major study conducted by (Saives et al, 2015) focused on improving the independence of residents with specific health conditions via continuous monitoring of disease progression as characterized by changes in lifestyle via the recognition of activities performed by the resident. In order to achieve residents' goals, the researchers used a two-pronged approach.…”

Evaluation of Wellness Detection Techniques using Complex Activities Association for Smart Home Ambient

“…J. Saives., et al [17] have proposed a hybrid model based on association rule mining and extended finite automata for the mapping of activities found from the association rule mining. However, prior knowledge is required and temporal deviations are not considered for wellness detection.…”