Night-Time Monitoring System (eNightLog) for Elderly Wandering Behavior

Contactless monitoring of human behaviors in bed using RSSI signals

In this paper, contactless monitoring and classification of human activities and sleeping postures in bed using radio signals is presented. The major contribution of this work is the development of a contactless monitoring and classification system with a proposed framework that uses received signal strength indicator (RSSI) signals collected from only one wireless link, where different human activities and sleep postures, including (a) no one in the bed, (b) a man sitting on the bed, (c) sleeping on his back, (d) seizure sleeping, and (e) sleeping on his side, are tested. With our proposed system, there is no need to attach any sensors or medical devices to the human body or the bed. That is the limitation of the sensor-based technology. Additionally, our system does not raise a privacy concern, which is the major limitation of vision-based technology. Experiments using low-cost, low-power 2.4 GHz IEEE802.15.4 wireless networks have been conducted in laboratories. Results demonstrate that the proposed system can automatically monitor and classify human sleeping postures in real time. The average classification accuracy of activities and sleep postures obtained from different subjects, test environments, and hardware platforms is 99.92%, 98.87%, 98.01%, 87.57%, and 95.87% for cases (a) to (e), respectively. Here, the proposed system provides an average accuracy of 96.05%. Furthermore, the system can also monitor and separate the difference between the cases of the man falling from his bed and the man getting out of his bed. This autonomous system and sleep posture information can thus be used to support care people, physicians, and medical staffs in the evaluation and planning of treatment for the benefit of patients and related people. The proposed system for non-invasive monitoring and classification of human activities and sleeping postures in bed using RSSI signals.

Sensors for Human Physical Behaviour Monitoring

The understanding and measurement of physical behaviours that occur in everyday life are essential not only for determining their relationship with health, but also for interventions, physical activity monitoring/surveillance of the population and specific groups, drug development, and developing public health guidelines and messages [...].

Vision Transformers (ViT) for Blanket-Penetrating Sleep Posture Recognition Using a Triple Ultra-Wideband (UWB) Radar System

Sleep posture has a crucial impact on the incidence and severity of obstructive sleep apnea (OSA). Therefore, the surveillance and recognition of sleep postures could facilitate the assessment of OSA. The existing contact-based systems might interfere with sleeping, while camera-based systems introduce privacy concerns. Radar-based systems might overcome these challenges, especially when individuals are covered with blankets. The aim of this research is to develop a nonobstructive multiple ultra-wideband radar sleep posture recognition system based on machine learning models. We evaluated three single-radar configurations (top, side, and head), three dual-radar configurations (top + side, top + head, and side + head), and one tri-radar configuration (top + side + head), in addition to machine learning models, including CNN-based networks (ResNet50, DenseNet121, and EfficientNetV2) and vision transformer-based networks (traditional vision transformer and Swin Transformer V2). Thirty participants (n = 30) were invited to perform four recumbent postures (supine, left side-lying, right side-lying, and prone). Data from eighteen participants were randomly chosen for model training, another six participants' data (n = 6) for model validation, and the remaining six participants' data (n = 6) for model testing. The Swin Transformer with side and head radar configuration achieved the highest prediction accuracy (0.808). Future research may consider the application of the synthetic aperture radar technique.

Depth-Camera-Based Under-Blanket Sleep Posture Classification Using Anatomical Landmark-Guided Deep Learning Model

Emerging sleep health technologies will have an impact on monitoring patients with sleep disorders. This study proposes a new deep learning model architecture that improves the under-blanket sleep posture classification accuracy by leveraging the anatomical landmark feature through an attention strategy. The system used an integrated visible light and depth camera. Deep learning models (ResNet-34, EfficientNet B4, and ECA-Net50) were trained using depth images. We compared the models with and without an anatomical landmark coordinate input generated with an open-source pose estimation model using visible image data. We recruited 120 participants to perform seven major sleep postures, namely, the supine posture, prone postures with the head turned left and right, left- and right-sided log postures, and left- and right-sided fetal postures under four blanket conditions, including no blanket, thin, medium, and thick. A data augmentation technique was applied to the blanket conditions. The data were sliced at an 8:2 training-to-testing ratio. The results showed that ECA-Net50 produced the best classification results. Incorporating the anatomical landmark features increased the F1 score of ECA-Net50 from 87.4% to 92.2%. Our findings also suggested that the classification performances of deep learning models guided with features of anatomical landmarks were less affected by the interference of blanket conditions.

Effects and implementation of a minimized physical restraint program for older adults in nursing homes: A pilot study

Purpose

Physical restraint (PR) reduction interventions are currently exploring in developed regions with well-established welfare systems, whereas developing countries with fast population aging have not attracted enough attention. This China's pilot study evaluated the effects of a minimized PR program on restraint reduction and nursing assistants' knowledge, attitudes, intention, and practice toward PR and explored nursing assistants' experience of the program.

Patients and methods

This was a one-group, pretest, and posttest pilot trial with a nested qualitative descriptive study. A minimized PR program was obtained by summarizing the best evidence and was implemented in one Chinese nursing home with 102 older adults from December 18, 2020, to March 21, 2021. An educational program including three theoretical lectures and one operation training was first conducted for nursing assistants one-month period. The primary outcome was PR rate at 3 months. The secondary outcomes contained duration of restraints, types of restraints, the rate of correct PR use, the incidence of falls and/or fall-related injuries, and antipsychotics use at 3 months. Data on PR use and older adults' characteristics were collected through physical restraints observation forms and older adults' medical records. Nursing assistants' knowledge, attitude, intention, and practice toward PR were measured using the Staff Knowledge, Attitudes, and Practices Questionnaire regarding PR at 1 month. A semi-structured interview for two administrative staff and a focus group discussion with 13 nursing assistants were analyzed using content analysis to explore perspectives of intervention implementation at 3 months.

Results

There were a significant increase in knowledge, attitude, and practice and a decrease in intention of nursing assistants after 1-month educational intervention (P < 0.001). Furthermore, only the rate of correct PR increased and the duration of restraint in the daytime decreased significantly at 3 months (P < 0.05). There were no significant effects on PR rate and other secondary outcomes at follow-up. Qualitatively, nursing assistants demonstrated overtly supportive perspectives and that assistance from the program enhanced their knowledge and practice. They noted several challenges that impeded implementation.

Conclusion

The intervention has acknowledged some benefits and was valued by nursing assistants. Implementation barriers should be addressed before delivering in larger trials.

Dissonance Between Human Nurses And Technology: Understanding Nurses’ Experience Using Technology Beds With Monitoring Functions Within Clinical Nursing Practice

Aims:

Are nurses adapting to the mechanized nursing practice environment? Is it possible for nurses to collaborate with technology to provide care to patients? The aim of the study is to investigate what nurses feel about using technology in nursing practice.

Background:

Preventing patients from falling is one of the nursing tasks that can be helped by using technology, such as sensors. However, little is known about how nurses experience and feel the use of technological beds for monitoring functionality within clinical nursing practice. Especially it is indicated that alarm fatigue makes nurses and patients fatigued and induces a dissonance between nurses and technology.

Objective:

To clarify the experiences of nurses in clinical practice following the introduction of a bed with monitoring and fall prevention technology (technology bed).

Methods:

We interviewed 12 nurses working at a hospital about their nursing practice experiences with the technology bed.

Results:

The content of the interview was classified into three categories: ‘providing a safe environment’, ‘limitation of entry into machine care scenes’, and ‘nurses’ dilemmas’; with eight themes describing nursing practice: (1) strategies of fall prevention, (2) decrease in nurses’ burden, (3) not good at using technology (all tools must be easy to use), (4) inefficiency such as over-engineering, (5) patients feel annoyed by frequent visits from nurses, (6) limitations of utilization from a nursing perspective, (7) nurse resistance to equipment introduction and (8) ethical issues.

Conclusion:

Although technology beds could effectively prevent falls, many nurses face an ethical dilemma in using these beds. It would be important for nurses to recognize the role of technology, embrace it, and raise awareness of collaborating with technology to eliminate a dissonance between technology and nurses.

A Survey of Mobile Apps for the Care Management of Patients with Dementia

Objective: Dementia is a progressive neurocognitive disorder that currently affects approximately 50 million people globally and causes a heavy burden for their families and societies. This study analyzed mobile apps for dementia care in different languages and during the COVID-19 pandemic. Methods: We searched PubMed, Cochrane Collaboration Central Register of Con-trolled Clinical Trials, Cochrane Systematic Reviews, Google Play Store, Apple App Store, and Huawei App Store for mobile applications for dementia care. The Mobile Application Rating Scale (MARS) was used to assess the quality of applications. Results: We included 99 apps for dementia care. No significant difference in MARS scores was noted between the two language apps (Overall MARS: English: 3.576 ± 0.580, Chinese: 3.569 ± 0.746, p = 0.962). In the subscale analysis, English apps had higher scores of perceived impact than Chinese apps but these were not significant (2.654 ± 1.372 vs. 2.000 ± 1.057, p = 0.061). (2) Applications during the COVID-19 pandemic had higher MARS scores than those before the COVID-19 pandemic but these were not significant (during the COVID-19 pandemic: 3.722 ± 0.416; before: 3.699 ± 0.615, p = 0.299). In the sub-scale analysis, apps during the COVID-19 pandemic had higher scores of engagement than apps before the COVID-19 pandemic but these were not significant (3.117 ± 0.594 vs. 2.698 ± 0.716, p = 0.068). Conclusions: Our results revealed that there is a minor but nonsignificant difference between different languages and during the COVID-19 pandemic. Further cooperation among dementia professionals, technology experts, and caregivers is warranted to provide evidence-based and user-friendly information to meet the needs of users.

A Night-Time Monitoring System (eNightLog) to Prevent Elderly Wandering in Hostels: A Three-Month Field Study

Older people are increasingly dependent on others to support their daily activities due to geriatric symptoms such as dementia. Some of them stay in long-term care facilities. Elderly people with night wandering behaviour may lose their way, leading to a significant risk of injuries. The eNightLog system was developed to monitor the night-time bedside activities of older people in order to help them cope with this issue. It comprises a 3D time-of-flight near-infrared sensor and an ultra-wideband sensor for detecting human presence and to determine postures without a video camera. A threshold-based algorithm was developed to classify different activities, such as leaving the bed. The system is able to send alarm messages to caregivers if an elderly user performs undesirable activities. In this study, 17 sets of eNightLog systems were installed in an elderly hostel with 17 beds in 9 bedrooms. During the three-month field test, 26 older people with different periods of stay were included in the study. The accuracy, sensitivity and specificity of detecting non-assisted bed-leaving events was 99.8%, 100%, and 99.6%, respectively. There were only three false alarms out of 2762 bed-exiting events. Our results demonstrated that the eNightLog system is sufficiently accurate to be applied in the hostel environment. Machine learning with instance segmentation and online learning will enable the system to be used for widely different environments and people, with improvements to be made in future studies.

Difference of Physical Restraint Knowledge, Attitudes and Practice Between Nurses and Nursing Assistants in Long-Term Care Facilities: A Cross-Sectional Study

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Purpose

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Wrist accelerometry for monitoring dementia agitation behaviour in clinical settings: A scoping review

Agitated behaviour among elderly people with dementia is a challenge in clinical management. Wrist accelerometry could be a versatile tool for making objective, quantitative, and long-term assessments. The objective of this review was to summarise the clinical application of wrist accelerometry to agitation assessments and ways of analysing the data. Two authors independently searched the electronic databases CINAHL, PubMed, PsycInfo, EMBASE, and Web of Science. Nine (n = 9) articles were eligible for a review. Our review found a significant association between the activity levels (frequency and entropy) measured by accelerometers and the benchmark instrument of agitated behaviour. However, the performance of wrist accelerometry in identifying the occurrence of agitation episodes was unsatisfactory. Elderly people with dementia have also been monitored in existing studies by investigating the at-risk time for their agitation episodes (daytime and evening). Consideration may be given in future studies on wrist accelerometry to unifying the parameters of interest and the cut-off and measurement periods, and to using a sampling window to standardise the protocol for assessing agitated behaviour through wrist accelerometry.

A Blanket Accommodative Sleep Posture Classification System Using an Infrared Depth Camera: A Deep Learning Approach with Synthetic Augmentation of Blanket Conditions

Surveillance of sleeping posture is essential for bed-ridden patients or individuals at-risk of falling out of bed. Existing sleep posture monitoring and classification systems may not be able to accommodate the covering of a blanket, which represents a barrier to conducting pragmatic studies. The objective of this study was to develop an unobtrusive sleep posture classification that could accommodate the use of a blanket. The system uses an infrared depth camera for data acquisition and a convolutional neural network to classify sleeping postures. We recruited 66 participants (40 men and 26 women) to perform seven major sleeping postures (supine, prone (head left and right), log (left and right) and fetal (left and right)) under four blanket conditions (thick, medium, thin, and no blanket). Data augmentation was conducted by affine transformation and data fusion, generating additional blanket conditions with the original dataset. Coarse-grained (four-posture) and fine-grained (seven-posture) classifiers were trained using two fully connected network layers. For the coarse classification, the log and fetal postures were merged into a side-lying class and the prone class (head left and right) was pooled. The results show a drop of overall F1-score by 8.2% when switching to the fine-grained classifier. In addition, compared to no blanket, a thick blanket reduced the overall F1-scores by 3.5% and 8.9% for the coarse- and fine-grained classifiers, respectively; meanwhile, the lowest performance was seen in classifying the log (right) posture under a thick blanket, with an F1-score of 72.0%. In conclusion, we developed a system that can classify seven types of common sleeping postures under blankets and achieved an F1-score of 88.9%.