@inproceedings { , title = {Employing multi-modal sensors for personalised smart home health monitoring.}, abstract = {As the prevalence of IoT sensor equipment in smart homes continues to rise, long term monitoring for personalised and more representative health tracking has become more accessible. The estimation of physiological health factors such as gait and heart rate can be captured using a range of diverse sensor equipment, while behavioural changes are now being monitored using simple binary sensors through activity classification and profiling. Combining both physiological and behavioural monitoring in fixed layout properties has already allowed us to effectively consider fall risk. However, expanding application of the system to new layouts and conditions requires consideration of differing retro fit home layouts and sensor configurations. A wider selection of sensors in varying configurations could potentially allow for the identification of other health conditions such as heart disease and stroke.}, conference = {27th International conference on case-based reasoning workshop (ICCBR-WS19), co- located with the 27th International conference 27th International conference on case-based reasoning (ICCBR19)}, note = {INFO COMPLETE (Info via Scopus alert 3/4/2020 LM) PERMISSION GRANTED (version = VoR; embargo = none; licence = BY) DOCUMENT READY (VoR downloaded 7/4/2020 LM) ADDITIONAL INFO - Contact: Glenn Forbes}, pages = {185-190}, publicationstatus = {Published}, publisher = {CEUR Workshop Proceedings}, url = {https://rgu-repository.worktribe.com/output/891476}, keyword = {Health & Wellbeing, Smart homes, Sensors, Time-series, Data, Human activity recognition, Long term health monitoring}, year = {2020}, author = {Forbes, Glenn} editor = {Kapetanakis, Stelios and Borck, Hayley} }