Towards a hierarchically-structured decision support tool for improving seniors' independent living

Billis, Antonis; Papageorgiou, Elpiniki I.; Frantzidιs, Christos A.; Konstantinidis, Evdokimos I.; Bamidis, Panagiotis D.

doi:10.1145/2504335.2504362

Cited by 11 publications

(8 citation statements)

References 10 publications

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“…It aims to provide an advanced and affordable health-care assistance in a smart home environment, adopting a three-layer architecture and an unobtrusive sensor network to support a Decision Support System (DSS) for providing inputs to monitoring apps by user-friendly interaction [ 23 ]. A limited set of sensors and devices, such as a wrist wearable unit, a camera, a microphone and a Kinect sensor, are used in a SH setting to identify the basic physical activities (lying, sitting, walking, standing, cycling, running, ascending and descending stairs) of elderly people [ 24 ]. A low cost off-the-self system and open source platforms are developed to facilitate the generation of applications addressing the gap between advanced technologies and aging population.…”

Section: Smart Home Projects and Applicationsmentioning

confidence: 99%

The Elderly’s Independent Living in Smart Homes: A Characterization of Activities and Sensing Infrastructure Survey to Facilitate Services Development

Hernando

Pau

2015

Sensors

236

149

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Human activity detection within smart homes is one of the basis of unobtrusive wellness monitoring of a rapidly aging population in developed countries. Most works in this area use the concept of “activity” as the building block with which to construct applications such as healthcare monitoring or ambient assisted living. The process of identifying a specific activity encompasses the selection of the appropriate set of sensors, the correct preprocessing of their provided raw data and the learning/reasoning using this information. If the selection of the sensors and the data processing methods are wrongly performed, the whole activity detection process may fail, leading to the consequent failure of the whole application. Related to this, the main contributions of this review are the following: first, we propose a classification of the main activities considered in smart home scenarios which are targeted to older people’s independent living, as well as their characterization and formalized context representation; second, we perform a classification of sensors and data processing methods that are suitable for the detection of the aforementioned activities. Our aim is to help researchers and developers in these lower-level technical aspects that are nevertheless fundamental for the success of the complete application.

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Section: Smart Home Projects and Applicationsmentioning

confidence: 99%

The Elderly’s Independent Living in Smart Homes: A Characterization of Activities and Sensing Infrastructure Survey to Facilitate Services Development

Hernando

Pau

2015

Sensors

236

149

View full text Add to dashboard Cite

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“…SWEET-HOME, a French project, aimed at developing an assisted living technology mainly based on audio analysis [13]. Billias et al [14] adopted ambient sensors, such as cameras and microphones, to analyse the daily activities of older users. Recently, Lio [15], a personal robot assistant developed by F&P Robotics, was introduced with a multifunctional arm.…”

Section: Introductionmentioning

confidence: 99%

Ultra-Wideband Radar-Based Activity Recognition Using Deep Learning

Noori¹,

Uddin

Tørresen

2021

IEEE Access

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With recent advances in the field of sensing, it has become possible to build better assistive technologies. This enables the strengthening of eldercare with regard to daily routines and the provision of personalised care to users. For instance, it is possible to detect a person's behaviour based on wearable or ambient sensors; however, it is difficult for users to wear devices 24/7, as they would have to be recharged regularly because of their energy consumption. Similarly, although cameras have been widely used as ambient sensors, they carry the risk of breaching users' privacy. This paper presents a novel sensing approach based on deep learning for human activity recognition using a non-wearable ultra-wideband (UWB) radar sensor. UWB sensors protect privacy better than RGB cameras because they do not collect visual data. In this study, UWB sensors were mounted on a mobile robot to monitor and observe subjects from a specific distance (namely, 1.5-2.0 m). Initially, data were collected in a lab environment for five different human activities. Subsequently, the data were used to train a model using the state-of-the-art deep learning approach, namely long short-term memory (LSTM). Conventional training approaches were also tested to validate the superiority of LSTM. As a UWB sensor collects many data points in a single frame, enhanced discriminant analysis was used to reduce the dimensions of the features through application of principal component analysis to the raw dataset, followed by linear discriminant analysis. The enhanced discriminant features were fed into the LSTMs. Finally, the trained model was tested using new inputs. The proposed LSTM-based activity recognition approach performed better than conventional approaches, with an accuracy of 99.6%. We applied 5-fold cross-validation to test our approach. We also validated our approach on publically available dataset. The proposed method can be applied in many prominent fields, including human-robot interaction for various practical applications, such as mobile robots for eldercare.

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“…During the last few decades, many researchers have tried to carry out smart home projects [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. For instance, GatorTech [ 19 ] is an earlier smart home project that was developed at the University of Florida.…”

Section: Introductionmentioning

confidence: 99%

“…The Markov Logic Network has been a prominent approach in research on smart homes for context-aware decision processes for coping with uncertain events that are predicted from sensor data [ 22 ]. In [ 24 ], several ambient sensors such as cameras and microphones were combined to recognize elderly activities (e.g., lying, sitting, walking, standing, cycling, running, and ascending and descending stairs). In [ 25 ], the authors suggested a smart assistive living environment for facilitating the prolonged stay of elderly people at home.…”

Section: Introductionmentioning

confidence: 99%

Ambient Sensors for Elderly Care and Independent Living: A Survey

Uddin

Khaksar

Tørresen

2018

Sensors

141

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Elderly care at home is a matter of great concern if the elderly live alone, since unforeseen circumstances might occur that affect their well-being. Technologies that assist the elderly in independent living are essential for enhancing care in a cost-effective and reliable manner. Elderly care applications often demand real-time observation of the environment and the resident’s activities using an event-driven system. As an emerging area of research and development, it is necessary to explore the approaches of the elderly care system in the literature to identify current practices for future research directions. Therefore, this work is aimed at a comprehensive survey of non-wearable (i.e., ambient) sensors for various elderly care systems. This research work is an effort to obtain insight into different types of ambient-sensor-based elderly monitoring technologies in the home. With the aim of adopting these technologies, research works, and their outcomes are reported. Publications have been included in this survey if they reported mostly ambient sensor-based monitoring technologies that detect elderly events (e.g., activities of daily living and falls) with the aim of facilitating independent living. Mostly, different types of non-contact sensor technologies were identified, such as motion, pressure, video, object contact, and sound sensors. Besides, multicomponent technologies (i.e., combinations of ambient sensors with wearable sensors) and smart technologies were identified. In addition to room-mounted ambient sensors, sensors in robot-based elderly care works are also reported. Research that is related to the use of elderly behavior monitoring technologies is widespread, but it is still in its infancy and consists mostly of limited-scale studies. Elderly behavior monitoring technology is a promising field, especially for long-term elderly care. However, monitoring technologies should be taken to the next level with more detailed studies that evaluate and demonstrate their potential to contribute to prolonging the independent living of elderly people.

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Towards a hierarchically-structured decision support tool for improving seniors' independent living

Cited by 11 publications

References 10 publications

The Elderly’s Independent Living in Smart Homes: A Characterization of Activities and Sensing Infrastructure Survey to Facilitate Services Development

The Elderly’s Independent Living in Smart Homes: A Characterization of Activities and Sensing Infrastructure Survey to Facilitate Services Development

Ultra-Wideband Radar-Based Activity Recognition Using Deep Learning

Ambient Sensors for Elderly Care and Independent Living: A Survey

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