Assessing Human Activity in Elderly People Using Non-Intrusive Load Monitoring

Abstract

Abstract:The ageing of the population, and their increasing wish of living independently, are motivating the development of welfare and healthcare models. Existing approaches based on the direct heath-monitoring using body sensor networks (BSN) are precise and accurate. Nonetheless, their intrusiveness causes non-acceptance. New approaches seek the indirect monitoring through monitoring activities of daily living (ADLs), which proves to be a suitable solution. ADL monitoring systems use many heterogeneous sensors, are less intrusive, and are less expensive than BSN, however, the deployment and maintenance of wireless sensor networks (WSN) prevent them from a widespread acceptance. In this work, a novel technique to monitor the human activity, based on non-intrusive load monitoring (NILM), is presented. The proposal uses only smart meter data, which leads to minimum intrusiveness and a potential massive deployment at minimal cost. This could be the key to develop sustainable healthcare models for smart homes, capable of complying with the elderly people' demands. This study also uses the Dempster-Shafer theory to provide a daily score of normality with regard to the regular behavior. This approach has been evaluated using real datasets and, additionally, a benchmarking against a Gaussian mixture model approach is presented.

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

“…A total of 83% of the scientific papers selected and summarized in Table S8, presented in the Supplementary Materials file, focus their research exclusively on smart homes, while the remaining 17% analyze both smart homes and smart buildings in general. In these papers, the authors make use of different types of sensors, including smartphone sensors [88]; electroglottography (EGG) electrodes [88]; smart meters [35,87]; wearable sensors providing inertial data, environment sensors and data processed video streams [89]; electricity, water and natural gas consumption sensors [90]; and multi-appliance recognition systems, designing a single smart meter using a current sensor and a voltage sensor in combination with a microprocessor to meter multi-appliances [64].…”

“…With respect to the reasons for implementing the GPR integrated with sensor devices in smart buildings, these are mainly related to human activity recognition/monitoring [35,[87][88][89]; voice pathology assessment [88]; monitoring of human health [89]; ambient assisted living [35]; recognizing household appliances in order to assess their usage and develop habits of power preservation [64]; and developing a framework for automatic leakage detection in smart water and gas grids [90].…”

“…With respect to the devised research methods, in [ The performance metrics chosen by the authors of papers using Gaussian Process Regression (GPR) integrated with sensor devices in smart buildings included Score for test events [87]; Accuracy [88]; Average Error [89]; True Positive Percentage (TPP), False Positive Percentage (FPP), Precision, Recall, F1 Score, and F2 Score [35]; the probability of correctly detecting an anomaly, the probability of erroneously detecting an anomaly, the Receiver Operating Characteristic (ROC) curve, and Area Under the ROC Curve (AUC) [90]; and Accuracy, the Success Rate and the Recognition Rate [64].…”

“…Acknowledging the importance of human activity monitoring in ensuring a certain level of independence for the elderly without sacrificing their wellbeing, in [87], Alcalá et al aimed to overcome the challenges arising from the rejection of intrusive monitoring techniques due to privacy issues by the residents of smart homes. To this end, the authors proposed a Non-Intrusive Load Monitoring (NILM) algorithm developed based on the Dempster-Shafer theory using only the data retrieved from a smart metering device, and compared this with the Gaussian Mixture model using the Score for Test Events as a performance metric.…”

Detection of Residents’ Abnormal Behaviour by Analysing Energy Consumption of Individual Households

“…Alcála et al [15] focus on using energy consumption to determine if the residents behaviour is deviating from the norm. The authors use Non-Intrusive Monitor Loading (NILM), which takes a household's aggregated energy consumption and disaggregates it into individual appliances.…”