Simulation of Smart Home Activity Datasets

Synnott, Jonathan; Nugent, Chris; Jeffers, W. Paul

doi:10.3390/s150614162

Cited by 60 publications

(54 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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, ).…”

Section: Background and Related Workmentioning

confidence: 99%

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

2019

View full text Add to dashboard Cite

Older adults with early‐stage dementia (ED) can experience confusion or lack clarity when performing routine activities of daily living (ADLs). These circumstances predispose the older adult to safety‐critical and often risky situations. A safety‐critical risky situation is one that constitutes a hazard. To support independent living, a sensor‐laden smart environment can be employed to mitigate such hazards. In this paper, we propose a situation‐centered goal reinforcement framework that supports older adults with ED in their decision making, and guides them through their ADL in order to fulfill their goal or intention and avoid hazards. First, we employ an LSTM (Long Short‐Term Memory) model to infer the current goal of the resident, using their previously observed normal ADL patterns. Secondly, we identify potentially risky situations in their currently observed goal path. We then incorporate a situ‐learning agent (SLA) that helps an inhabitant to make the right decision, thus preventing adverse events while guiding her through the task sequence that leads to her goal state. In addition, we use a naïve agent to simulate episodes of confusion similar to those that might be experienced by older adults with ED. We validated our method against an open‐source dementia dataset (Quesada et al., 2015) by considering four types of ADLs as case studies. We achieved an accuracy of 90.1% for our goal inference model, higher than the accuracies reported by related studies. We also reported other metrics including precision, recall and f1‐score for goal inference model. Finally, SLA's action recommendations relevance was evaluated accordingly.

show abstract

Section: Background and Related Workmentioning

confidence: 99%

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

2019

View full text Add to dashboard Cite

show abstract

“…OpenSHS tries to provide a solution to the issues and challenges identified by Synnott et al [6]. OpenSHS follows a hybrid approach, to generate datasets, combining the advantages of both model-based and interactive approaches.…”

Section: Openshs Architecture and Implementationmentioning

confidence: 99%

OpenSHS: Open Smart Home Simulator

Alshammari

Sedky

et al. 2017

Sensors

View full text Add to dashboard Cite

This paper develops a new hybrid, open-source, cross-platform 3D smart home simulator, OpenSHS, for dataset generation. OpenSHS offers an opportunity for researchers in the field of the Internet of Things (IoT) and machine learning to test and evaluate their models. Following a hybrid approach, OpenSHS combines advantages from both interactive and model-based approaches. This approach reduces the time and efforts required to generate simulated smart home datasets. We have designed a replication algorithm for extending and expanding a dataset. A small sample dataset produced, by OpenSHS, can be extended without affecting the logical order of the events. The replication provides a solution for generating large representative smart home datasets. We have built an extensible library of smart devices that facilitates the simulation of current and future smart home environments. Our tool divides the dataset generation process into three distinct phases: first design: the researcher designs the initial virtual environment by building the home, importing smart devices and creating contexts; second, simulation: the participant simulates his/her context-specific events; and third, aggregation: the researcher applies the replication algorithm to generate the final dataset. We conducted a study to assess the ease of use of our tool on the System Usability Scale (SUS).

show abstract

“…To evaluate our module's ability to detect attacks, we used the CASAS dataset [50,51]. The CASAS dataset is a smart home based dataset.…”

Section: Evaluation Datasetmentioning

confidence: 99%

SH-SecNet: An Enhanced Secure Network Architecture for the Diagnosis of Security Threats in a Smart Home

2017

View full text Add to dashboard Cite

Abstract:The growing demand for an independent and comfortable lifestyle has motivated the development of the smart home, and providing security is a major challenge for developers and security analysts. Enhancing security in the home environment has been recognized as one of the main obstacles to realizing the vision of creating energy-efficient smart homes and buildings. Understanding the risks associated with the use and potential exploitation of information about homes, end-users, and partners, as well as forming techniques for integrating security assessments into the design, is not straightforward. To address this challenge, we propose enhanced secure network architecture (SH-SecNet) for the diagnosis of security threats in the smart home. In our architecture, we use the Multivariate Correlation Analysis (MCA) technique to analyze the network flow packet in the network layer, as this classifies the network traffic by extracting the correlation between network traffic features. We evaluated the performance of our architecture with respect to various parameters, such as CPU utilization, throughput, round trip time, and accuracy. The result of the evaluation shows that our architecture is efficient and accurate in detecting and mitigating attacks in the smart home network with a low performance overhead.

show abstract

Simulation of Smart Home Activity Datasets

Cited by 60 publications

References 57 publications

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

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

OpenSHS: Open Smart Home Simulator

SH-SecNet: An Enhanced Secure Network Architecture for the Diagnosis of Security Threats in a Smart Home

Contact Info

Product

Resources

About