Behavior analysis for elderly care using a network of low-resolution visual sensors

Eldib, Mohamed; Deboeverie, Francis; Philips, Wilfried; Aghajan, Hamid

doi:10.1117/1.jei.25.4.041003

Cited by 36 publications

(38 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using only video technology can also help in elderly care; video helped estimate locations and perform behavioral analysis under low-resolution constraints as an alternative to PIR sensors or high-resolution cameras. For example, [78] used an Agilent ADNS-3060 optical sensor (30 × 30 pixels) installed in a service apartment for senior citizens, projecting pattern identification for recovery periods through caregiver monitoring.…”

Section: Analysis and Discussionmentioning

confidence: 99%

Hardware for Recognition of Human Activities: A Review of Smart Home and AAL Related Technologies

Sanchez-Comas

Synnes

Hallberg

2020

Sensors

View full text Add to dashboard Cite

Activity recognition (AR) from an applied perspective of ambient assisted living (AAL) and smart homes (SH) has become a subject of great interest. Promising a better quality of life, AR applied in contexts such as health, security, and energy consumption can lead to solutions capable of reaching even the people most in need. This study was strongly motivated because levels of development, deployment, and technology of AR solutions transferred to society and industry are based on software development, but also depend on the hardware devices used. The current paper identifies contributions to hardware uses for activity recognition through a scientific literature review in the Web of Science (WoS) database. This work found four dominant groups of technologies used for AR in SH and AAL—smartphones, wearables, video, and electronic components—and two emerging technologies: Wi-Fi and assistive robots. Many of these technologies overlap across many research works. Through bibliometric networks analysis, the present review identified some gaps and new potential combinations of technologies for advances in this emerging worldwide field and their uses. The review also relates the use of these six technologies in health conditions, health care, emotion recognition, occupancy, mobility, posture recognition, localization, fall detection, and generic activity recognition applications. The above can serve as a road map that allows readers to execute approachable projects and deploy applications in different socioeconomic contexts, and the possibility to establish networks with the community involved in this topic. This analysis shows that the research field in activity recognition accepts that specific goals cannot be achieved using one single hardware technology, but can be using joint solutions, this paper shows how such technology works in this regard.

show abstract

Section: Analysis and Discussionmentioning

confidence: 99%

Hardware for Recognition of Human Activities: A Review of Smart Home and AAL Related Technologies

Sanchez-Comas

Synnes

Hallberg

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Using low-resolution sensors adopts a network of extremely low-resolution cameras [33]- [35] or low-resolution colour sensors [36] to capture low-resolution visual images. These sensors have been successfully exploited in the applications of activity recognition [33], behaviour understanding [34] and object localisation [36]. However, these sensors are more sensitive to the local changes in the light conditions [34], [36], which affects the reliability in HAR.…”

Section: A Privacy Presevationmentioning

confidence: 99%

Modeling Temporal Visual Salience for Human Action Recognition Enabled Visual Anonymity Preservation

2020

View full text Add to dashboard Cite

This paper proposes a novel approach for visually anonymizing video clips while retaining the ability to machine-based analysis of the video clip, such as, human action recognition. The visual anonymization is achieved by proposing a novel method for generating the anonymization silhouette by modelling the frame-wise temporal visual salience. This is followed by analysing these temporal saliencebased silhouettes by extracting the proposed histograms of gradients in salience (HOG-S) for learning the action representation in the visually anonymized domain. Since the anonymization maps are based on the temporal salience maps represented in gray scale, only the moving body parts related to the motion of the action are represented in larger gray values forming highly anonymized silhouettes, resulting in the highest mean anonymity score (MAS), the least identifiable visual appearance attributes and a high utility of human-perceived utility in action recognition. In terms of machine-based human action recognition, using the proposed HOGS features has resulted in the highest accuracy rate in the anonymized domain compared to those achieved from the existing anonymization methods. Overall, the proposed holistic human action recognition method, i.e., the temporal salience modelling followed by the HOGS feature extraction, has resulted in the best human action recognition accuracy rates for datasets DHA, KTH, UIUC1, UCF Sports and HMDB51 with improvements of 3%, 1.6%, 0.8%, 1.3% and 16.7%, respectively. The proposed method outperforms both feature-based and deep learning based existing approaches. INDEX TERMS Visual anonymization, Human action recognition, histogram of gradients in salience (HOG-S), temporal visual salience estimation, privacy, video-based monitoring, assisted living.

show abstract

“…Deviation or change in the physical activity level, according to historical data, was used as an early symptom of health problems. In general, the abnormalities in the human behaviour are mainly related to the detection of falls [ 53 , 54 ], inactive periods [ 55 , 56 ], abnormal living patterns [ 57 ], long-term behaviour changes [ 24 ], or abnormal events such as sleeping disorder [ 58 ].…”

Section: Monitoring Human Behaviourmentioning

confidence: 99%

A Behaviour Monitoring System (BMS) for Ambient Assisted Living

Eisa

Moreira

2017

Sensors

View full text Add to dashboard Cite

Unusual changes in the regular daily mobility routine of an elderly person at home can be an indicator or early symptom of developing health problems. Sensor technology can be utilised to complement the traditional healthcare systems to gain a more detailed view of the daily mobility of a person at home when performing everyday tasks. We hypothesise that data collected from low-cost sensors such as presence and occupancy sensors can be analysed to provide insights on the daily mobility habits of the elderly living alone at home and to detect routine changes. We validate this hypothesis by designing a system that automatically learns the daily room-to-room transitions and permanence habits in each room at each time of the day and generates alarm notifications when deviations are detected. We present an algorithm to process the sensors’ data streams and compute sensor-driven features that describe the daily mobility routine of the elderly as part of the developed Behaviour Monitoring System (BMS). We are able to achieve low detection delay with confirmation time that is high enough to convey the detection of a set of common abnormal situations. We illustrate and evaluate BMS with synthetic data, generated by a developed data generator that was designed to mimic different user’s mobility profiles at home, and also with a real-life dataset collected from prior research work. Results indicate BMS detects several mobility changes that can be symptoms of common health problems. The proposed system is a useful approach for learning the mobility habits at the home environment, with the potential to detect behaviour changes that occur due to health problems, and therefore, motivating progress toward behaviour monitoring and elder’s care.

show abstract

Behavior analysis for elderly care using a network of low-resolution visual sensors

Cited by 36 publications

References 53 publications

Hardware for Recognition of Human Activities: A Review of Smart Home and AAL Related Technologies

Hardware for Recognition of Human Activities: A Review of Smart Home and AAL Related Technologies

Modeling Temporal Visual Salience for Human Action Recognition Enabled Visual Anonymity Preservation

A Behaviour Monitoring System (BMS) for Ambient Assisted Living

Contact Info

Product

Resources

About