Prediction of mobility entropy in an Ambient Intelligent environment

Chernbumroong, Saisakul; Lotfi, Ahmad; Langensiepen, Caroline

doi:10.1109/ia.2014.7009460

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…The IM is defined as the frequency of the transition from room to room in a home environment. Readers are referred to [ 50 ] for more details about this measure. The features used as input to the SVM are the start time of entering to each location (room), the time spent in each room (duration), the encoded number of each room and the transitions from one room to another inside the house.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Exploring Entropy Measurements to Identify Multi-Occupancy in Activities of Daily Living

Howedi

Lotfi

Pourabdollah

2019

Entropy

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Human Activity Recognition (HAR) is the process of automatically detecting human actions from the data collected from different types of sensors. Research related to HAR has devoted particular attention to monitoring and recognizing the human activities of a single occupant in a home environment, in which it is assumed that only one person is present at any given time. Recognition of the activities is then used to identify any abnormalities within the routine activities of daily living. Despite the assumption in the published literature, living environments are commonly occupied by more than one person and/or accompanied by pet animals. In this paper, a novel method based on different entropy measures, including Approximate Entropy (ApEn), Sample Entropy (SampEn), and Fuzzy Entropy (FuzzyEn), is explored to detect and identify a visitor in a home environment. The research has mainly focused on when another individual visits the main occupier, and it is, therefore, not possible to distinguish between their movement activities. The goal of this research is to assess whether entropy measures can be used to detect and identify the visitor in a home environment. Once the presence of the main occupier is distinguished from others, the existing activity recognition and abnormality detection processes could be applied for the main occupier. The proposed method is tested and validated using two different datasets. The results obtained from the experiments show that the proposed method could be used to detect and identify a visitor in a home environment with a high degree of accuracy based on the data collected from the occupancy sensors.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Exploring Entropy Measurements to Identify Multi-Occupancy in Activities of Daily Living

Howedi

Lotfi

Pourabdollah

2019

Entropy

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“…Moreover, the same dataset is applied to the IM measure, which is a measure to calculate the frequency of movement from room to room in a home environment. For more details about this measure, readers are referred to Chernbumroong et al (2014). The ShEn, PerEn, and MPE are also compared with the MMPP model proposed in another study for visitor detection in the homes of older adults living alone (Aicha et al 2017).…”

Section: Comparison With Existing Modelling Techniquesmentioning

confidence: 99%

Employing entropy measures to identify visitors in multi-occupancy environments

Howedi

Lotfi

Pourabdollah

2020

J Ambient Intell Human Comput

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Human activity recognition (HAR) is used to support older adults to live independently in their own homes. Once activities of daily living (ADL) are recognised, gathered information will be used to identify abnormalities in comparison with the routine activities. Ambient sensors, including occupancy sensors and door entry sensors, are often used to monitor and identify different activities. Most of the current research in HAR focuses on a single-occupant environment when only one person is monitored, and their activities are categorised. The assumption that home environments are occupied by one person all the time is often not true. It is common for a resident to receive visits from family members or health care workers, representing a multi-occupancy environment. Entropy analysis is an established method for irregularity detection in many applications; however, it has been rarely applied in the context of ADL and HAR. In this paper, a novel method based on different entropy measures, including Shannon Entropy, Permutation Entropy, and Multiscale-Permutation Entropy, is employed to investigate the effectiveness of these entropy measures in identifying visitors in a home environment. This research aims to investigate whether entropy measures can be utilised to identify a visitor in a home environment, solely based on the information collected from motion detectors [e.g., passive infra-red] and door entry sensors. The entropy measures are tested and evaluated based on a dataset gathered from a real home environment. Experimental results are presented to show the effectiveness of entropy measures to identify visitors and the time of their visits without the need for employing extra wearable sensors to tag the visitors. The results obtained from the experiments show that the proposed entropy measures could be used to detect and identify a visitor in a home environment with a high degree of accuracy.

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“…Most literature focusing on ambient sensing to monitor cognitive health of behavioural disturbances have reported on technology development and feasibility studies with older adults and PwDs [ 46 , 47 , 48 , 49 ]. The literature has traditionally examined routine-action monitoring such as posture analysis [ 50 , 51 , 52 , 53 , 54 , 55 , 56 ] and safety monitoring [ 57 ], fall detection (e.g., [ 58 , 59 , 60 , 61 , 62 , 63 ], indoor localization (e.g., [ 64 , 65 , 66 ], and wandering (e.g., [ 67 ]). Healthcare professionals’ perspectives on the use of ambient sensing technologies have been explored in a variety of settings, including at a people’s home [ 68 , 69 ], in laboratories [ 70 , 71 ] and in healthcare institutions [ 72 , 73 ].…”

Section: Introductionmentioning

confidence: 99%

Healthcare Professionals’ Perspective on Implementing a Detector of Behavioural Disturbances in Long-Term Care Homes

Choukou

Mbabaali

East

2021

IJERPH

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The number of Canadians with dementia is expected to rise to 674,000 in the years to come. Finding ways to monitor behavioural disturbance in patients with dementia (PwDs) is crucial. PwDs can unintentionally behave in ways that are harmful to them and the people around them, such as other residents or care providers. Current practice does not involve technology to monitor PwD behaviours. Events are reported randomly by nonstaff members or when a staff member notices the absence of a PwD from a scheduled event. This study aims to explore the potential of implementing a novel detector of behavioural disturbances (DBD) in long-term care homes by mapping the perceptions of healthcare professionals and family members about this technology. Qualitative information was gathered from a focus group involving eight healthcare professionals working in a tertiary care facility and a partner of a resident admitted in the same facility. Thematic analysis resulted in three themes: (A) the ability of the DBD to detect relevant dementia-related behavioural disturbances that are typical of PwD; (B) the characteristics of the DBD and clinical needs and preferences; (C) the integration of the DBD into daily routines. The results tend to confirm the adequacy of the DBD to the day-to-day needs for the detection of behavioural disturbances and hazardous behaviours. The DBD was considered to be useful and easy to use in the tertiary care facility examined in this study. The participants intend to use the DBD in the future, which means that it has a high degree of acceptance.

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Prediction of mobility entropy in an Ambient Intelligent environment

Cited by 7 publications

References 14 publications

Exploring Entropy Measurements to Identify Multi-Occupancy in Activities of Daily Living

Exploring Entropy Measurements to Identify Multi-Occupancy in Activities of Daily Living

Employing entropy measures to identify visitors in multi-occupancy environments

Healthcare Professionals’ Perspective on Implementing a Detector of Behavioural Disturbances in Long-Term Care Homes

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