Low cost and batteryless sensor-enabled radio frequency identification tag based approaches to identify patient bed entry and exit posture transitions

INTRODUCTION

Falls in hospitals and residential care facilities commonly occur near the bed. The aim of this study was to investigate the accuracy of a continuously wearable, batteryless, low power and low cost monitoring device (Wearable Wireless Identification and Sensing Platform) with a single kinematic sensor capable of real-time monitoring to automatically detect bed entry and exit events.

MATERIALS AND METHODS

Three dimensional acceleration readings and the strength of the transmitted signal from the WISP was interpreted to identify bed exit events and sensitivity, specificity and Receiving Operator Curves (ROC) were determined.

RESULTS

The sensor located over sternum method performed best with sensitivity and specificity values of 92.8% and 97.5% respectively for detecting bed entry and values of 90.4% and 93.80% respectively for bed exit. On the other hand, the sensor-on-mattress algorithm achieved sensitivity and specificity values of 84.2% and 97.4% respectively for bed entry and 79% and 97.4% for bed exit detection.

CONCLUSION

The WISP located over the sternum method is the preferred method to detect bed entry and exit. However, further work in frail older people is required to confirm the performance of this method.

Memristive crypto primitive for building highly secure physical unclonable functions

Physical unclonable functions (PUFs) exploit the intrinsic complexity and irreproducibility of physical systems to generate secret information. The advantage is that PUFs have the potential to provide fundamentally higher security than traditional cryptographic methods by preventing the cloning of devices and the extraction of secret keys. Most PUF designs focus on exploiting process variations in Complementary Metal Oxide Semiconductor (CMOS) technology. In recent years, progress in nanoelectronic devices such as memristors has demonstrated the prevalence of process variations in scaling electronics down to the nano region. In this paper, we exploit the extremely large information density available in nanocrossbar architectures and the significant resistance variations of memristors to develop an on-chip memristive device based strong PUF (mrSPUF). Our novel architecture demonstrates desirable characteristics of PUFs, including uniqueness, reliability, and large number of challenge-response pairs (CRPs) and desirable characteristics of strong PUFs. More significantly, in contrast to most existing PUFs, our PUF can act as a reconfigurable PUF (rPUF) without additional hardware and is of benefit to applications needing revocation or update of secure key information.

Framework for preventing falls in acute hospitals using passive sensor enabled radio frequency identification technology

We describe a distributed architecture for a real-time falls prevention framework capable of providing a technological intervention to mitigate the risk of falls in acute hospitals through the development of an AmbIGeM (Ambient Intelligence Geritatric Management system). Our approach is based on using a battery free, wearable sensor enabled Radio Frequency Identification device. Unsupervised classification of high risk falls activities are used to facilitate an immediate response from caregivers by alerting them of the high risk activity, the particular patient, and their location. Early identification of high risk falls activities through a longitudinal and unsupervised setting in real-time allows the preventative intervention to be administered in a timely manner. Furthermore, real-time detection allows emergency protocols to be deployed immediately in the event of a fall. Finally, incidents of high risk activities are automatically documented to allow clinicians to customize and optimize the delivery of care to suit the needs of patients identified as being at most risk.

Effectiveness of an Ambient Intelligent Geriatric Management system (AmbIGeM) to prevent falls in older people in hospitals: protocol for the AmbIGeM stepped wedge pragmatic trial

Background

Although current best practice recommendations contribute to falls prevention in hospital, falls and injury rates remain high. There is a need to explore new interventions to reduce falls rates, especially in geriatric and general medical wards where older patients and those with cognitive impairment are managed.

Design and methods

A three-cluster stepped wedge pragmatic trial, with an embedded qualitative process, of the Ambient Intelligent Geriatric Management (AmbIGeM) system (wearable sensor device to alert staff of patients undertaking at-risk activities), for preventing falls in older patients compared with standard care. The trial will occur on three acute/subacute wards in two hospitals in Adelaide and Perth, Australia.

Participants

Patients aged >65 years admitted to study wards. A waiver (Perth) and opt-out of consent (Adelaide) was obtained for this study. Patients requiring palliative care will be excluded.

Outcomes

The primary outcome is falls rate; secondary outcome measures are: (1) proportion of participants falling; (2) rate of injurious inpatient falls/1000 participant bed-days; (3) acceptability and safety of the interventions from patients and clinical staff perspectives; and (4) hospital costs, mortality and use of residential care to 3 months postdischarge.

Discussion

This study investigates a novel technological approach to preventing falls in hospitalised older people. We hypothesise that the AmbIGeM intervention will reduce falls and injury rates, with an economic benefit attributable to the intervention. If successful, the AmbIGeM system will be a useful addition to falls prevention in hospital wards with high proportions of older people and people with cognitive impairment.

Trial registration number

Australian and New Zealand Clinical Trial Registry: ACTRN 12617000981325; Pre-results.

Effectiveness of the Wearable Sensor based Ambient Intelligent Geriatric Management System (AmbIGeM) in Preventing Falls in Older People in Hospitals

Background

The Ambient Intelligent Geriatric Management (AmbIGeM) system augments best practice and involves a novel wearable sensor (accelerometer and gyroscope) worn by patients where the data captured by the sensor are interpreted by algorithms to trigger alerts on clinician handheld mobile devices when risk movements are detected.

Methods

A 3-cluster stepped-wedge pragmatic trial investigating the effect on the primary outcome of falls rate and secondary outcome of injurious fall and proportion of fallers. Three wards across 2 states were included. Patients aged ≥65 years were eligible. Patients requiring palliative care were excluded. The trial was registered with the Australia and New Zealand Clinical Trials registry, number 12617000981325.

Results

A total of 4924 older patients were admitted to the study wards with 1076 excluded and 3240 (1995 control, 1245 intervention) enrolled. The median proportion of study duration with valid readings per patient was 49% ((interquartile range [IQR] 25%-67%)). There was no significant difference between intervention and control relating to the falls rate (adjusted rate ratio = 1.41, 95% confidence interval [0.85, 2.34]; p = .192), proportion of fallers (odds ratio = 1.54, 95% confidence interval [0.91, 2.61]; p = .105), and injurious falls rate (adjusted rate ratio = 0.90, 95% confidence interval [0.38, 2.14]; p = .807). In a post hoc analysis, falls and injurious falls rate were reduced in the Geriatric Evaluation and Management Unit wards when the intervention period was compared to the control period.

Conclusions

The AmbIGeM system did not reduce the rate of falls, rate of injurious falls, or proportion of fallers. There remains a case for further exploration and refinement of this technology given the post hoc analysis findings with the Geriatric Evaluation and Management Unit wards.

Clinical Trials Registration Number: 12617000981325

Care workers' voices in designing assistive technologies for preventing malnutrition in older people with dementia: Innovative Practice

Technology may be considered a way to promote nutritional health in people with dementia living in their home. For technologies to be effective and accepted by users, understanding technological needs prior to technology development is crucial; however, this necessitates greater research investment. Consequently, our focus is to derive needs for nutritional health promoting technologies for older people with dementia by understanding perceptions of care workers recruited by the aged care industry. In this paper, we provide a brief description of the theoretical framework that underpins the research study and the research methods selected. Significant learning outcomes related to the research methods include managing hierarchical relationships among participants, engagement with the care workers working in the community and using external material to spark discussion.

An Adaptive Markov Random Field for Structured Compressive Sensing

Exploiting intrinsic structures in sparse signals underpins the recent progress in compressive sensing (CS). The key for exploiting such structures is to achieve two desirable properties: generality (i.e., the ability to fit a wide range of signals with diverse structures) and adaptability (i.e., being adaptive to a specific signal). Most existing approaches, however, often only achieve one of these two properties. In this study, we propose a novel adaptive Markov random field sparsity prior for CS, which not only is able to capture a broad range of sparsity structures, but also can adapt to each sparse signal through refining the parameters of the sparsity prior with respect to the compressed measurements. To maximize the adaptability, we also propose a new sparse signal estimation where the sparse signals, support, noise and signal parameter estimation are unified into a variational optimization problem, which can be effectively solved with an alternative minimization scheme. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed method in recovery accuracy, noise tolerance, and runtime.

Towards falls prevention: a wearable wireless and battery-less sensing and automatic identification tag for real time monitoring of human movements

Falls related injuries among elderly patients in hospitals or residents in residential care facilities is a significant problem that causes emotional and physical trauma to those involved while presenting a rising healthcare expense in countries such as Australia where the population is ageing. Novel approaches using low cost and privacy preserving sensor enabled Radio Frequency Identification (RFID) technology may have the potential to provide a low cost and effective technological intervention to prevent falls in hospitals. We outline the details of a wearable sensor enabled RFID tag that is battery free, low cost, lightweight, maintenance free and can be worn continuously for automatic and unsupervised remote monitoring of activities of frail patients at acute hospitals or residents in residential care. The technological developments outlined in the paper forms part of an overall technological intervention developed to reduce falls at acute hospitals or in residential care facilities. This paper outlines the details of the technology, underlying algorithms and the results (where an accuracy of 94-100% was achieved) of a successful pilot trial.

Patient Acceptability of a Novel Technological Solution (Ambient Intelligent Geriatric Management System) to Prevent Falls in Geriatric and General Medicine Wards: A Mixed-Methods Study

Introduction

As effective interventions to prevent inpatient falls are lacking, a novel technological intervention was trialed. The Ambient Intelligent Geriatric Management (AmbIGeM) system used wearable sensors that detected and alerted staff of patient movements requiring supervision. While the system did not reduce falls rate, it is important to evaluate the acceptability, usability, and safety of the AmbIGeM system, from the perspectives of patients and informal carers.

Methods

We conducted a mixed-methods study using semistructured interviews, a pre-survey and post-survey. The AmbIGeM clinical trial was conducted in two geriatric evaluation and management units and a general medical ward, in two Australian hospitals, and a subset of participants were recruited. Within 3 days of being admitted to the study wards and enrolling in the trial, 31 participants completed the pre-survey. Prior to discharge (post-intervention), 30 participants completed the post-survey and 27 participants were interviewed. Interview data were thematically analyzed and survey data were descriptively analyzed.

Results

Survey and interview participants had an average age of 83 (SD 9) years, 65% were female, and 41% were admitted with a fall. Participants considered the AmbIGeM system a good idea. Most but not all thought the singlet and sensor component as acceptable and comfortable, with no privacy concerns. Participants felt reassured with extra monitoring, although sometimes misunderstood the purpose of AmbIGeM as detecting patient falls. Participants' acceptability was strongly positive, with median 8+ (0–10 scale) on pre- and post-surveys.

Discussion/Conclusion

Patients' acceptability is important to optimize outcomes. Overall older patients considered the AmbIGeM system as acceptable, usable, and improving safety. The findings will be important to guide refinement of this and other similar technology developments.

Findings from three methods to identify falls in hospitals: Results from the Ambient Intelligent Geriatric Management system fall prevention trial

OBJECTIVE

To (a) compare characteristics of patients who fall with those of patients who did not fall; and (b) characterise falls (time, injury severity and location) through three fall reporting methods (incident system reports, medical notes and clinician reports).

METHODS

A substudy design within a stepped-wedge clinical trial was used: 3239 trial participants were recruited from two inpatient Geriatric Evaluation and Management Units and one general medicine ward in two Australian states. To compare the characteristics of patients who had fallen with those who had not, descriptive tests were used. To characterise falls through three reporting methods, bivariate logistic regressions were used.

RESULTS

Patients who had fallen were more likely than patients who had not fallen to be cognitively impaired (51% vs. 29%, p < 0.01), admitted with falls (38% vs. 28%, p = 0.01) and have poor health outcomes such as prolonged length of stay (24 [16-34] vs. 12 [8-19] days [IQR], p < 0.01) and less likely to be discharged directly to the community (62% vs. 47%, p < 0.01). Most falls were captured from medical notes (93%), with clinician (71%) and incident reports (68%) missing 21%-25% of falls. The proportion of injurious falls identified through incident reports was higher than medical records or clinician reports (40% vs. 34% vs. 37%).

CONCLUSIONS

This study reaffirms the need to improve reporting falls in incident systems and at clinical handover to the team leader. Research should continue to use more than one method of identifying falls, but include data from medical records. Many falls cause injury, resulting in poor health outcomes.