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

Cost-Effectiveness and Value of Information Analysis of an Ambient Intelligent Geriatric Management (AmbIGeM) System Compared to Usual Care to Prevent Falls in Older People in Hospitals

BACKGROUND

The Ambient Intelligent Geriatric Management (AmbIGeM) system combines wearable sensors with artificial intelligence to trigger alerts to hospital staff before a fall. A clinical trial found no effect across a heterogenous population, but reported a reduction in the injurious falls rate in a post hoc analysis of patients on Geriatric Evaluation Management Unit (GEMU) wards. Cost-effectiveness and Value of Information (VoI) analyses of the AmbIGeM system in GEMU wards was undertaken.

METHODS

An Australian health-care system perspective and 5-year time horizon were used for the cost-effectiveness analysis. Implementation costs, inpatient costs and falls data were collected. Injurious falls were defined as causing bruising, laceration, fracture, loss of consciousness, or if the patient reported persistent pain. To compare costs and outcomes, generalised linear regression models were used to adjust for baseline differences between the intervention and usual care groups. Bootstrapping was used to represent uncertainty. For the VoI analysis, 10,000 different sample sizes with randomly sampled values ranging from 1 to 50,000 were tested to estimate the optimal sample size of a new trial that maximised the Expected Net Benefits of Sampling.

RESULTS

An adjusted 0.036 fewer injurious falls (adjusted rate ratio of 0.56) and AUD$4554 lower costs were seen in the intervention group. However, uncertainty that the intervention is cost effective for the prevention of an injurious fall was present at all monetary values of this effectiveness outcome. A new trial with a sample of 4376 patients was estimated to maximise the Expected Net Benefit of Sampling, generating a net benefit of AUD$186,632 at a benefit-to-cost ratio of 1.1.

CONCLUSIONS

The benefits to cost ratio suggests that a new trial of the AmbIGeM system in GEMU wards may not be high-value compared to other potential trials, and that the system should be implemented. However, a broader analysis of options for preventing falls in GEMU is required to fully inform decision making.

TRIAL REGISTRATION

Australian and New Zealand Clinical Trial Registry (ACTRN 12617000981325).

Deriving Multiple-Layer Information from a Motion-Sensing Mattress for Precision Care

Bed is often the personal care unit in hospitals, nursing homes, and individuals' homes. Rich care-related information can be derived from the sensing data from bed. Patient fall is a significant issue in hospitals, many of which are related to getting in and/or out of bed. To prevent bed falls, a motion-sensing mattress was developed for bed-exit detection. A machine learning algorithm deployed on the chip in the control box of the mattress identified the in-bed postures based on the on/off pressure pattern of 30 sensing areas to capture the users' bed-exit intention. This study aimed to explore how sleep-related data derived from the on/off status of 30 sensing areas of this motion-sensing mattress can be used for multiple layers of precision care information, including wellbeing status on the dashboard and big data analysis for living pattern clustering. This study describes how multiple layers of personalized care-related information are further derived from the motion-sensing mattress, including real-time in-bed/off-bed status, daily records, sleep quality, prolonged pressure areas, and long-term living patterns. Twenty-four mattresses and the smart mattress care system (SMCS) were installed in a dementia nursing home in Taiwan for a field trial. Residents' on-bed/off-bed data were collected for 12 weeks from August to October 2021. The SMCS was developed to display care-related information via an integrated dashboard as well as sending reminders to caregivers when detecting events such as bed exits and changes in patients' sleep and living patterns. The ultimate goal is to support caregivers with precision care, reduce their care burden, and increase the quality of care. At the end of the field trial, we interviewed four caregivers for their subjective opinions about whether and how the SMCS helped their work. The caregivers' main responses included that the SMCS helped caregivers notice the abnormal situation for people with dementia, communicate with family members of the residents, confirm medication adjustments, and whether the standard care procedure was appropriately conducted. Future studies are suggested to focus on integrated care strategy recommendations based on users' personalized sleep-related data.

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.

Implementation and experience of an innovative smart patient care system: a cross-sectional study

Background

Although a patient care system may help nurses handle patients’ requests or provide timely assistance to those in need, there are a number of barriers faced by nurses in handling alarms.

Methods

The aim of the study was to describe the implementation and experience of an innovative smart patient care system (SPCS). This study applied a cross-sectional descriptive design. We recruited 82 nurses from a medical center in Taiwan, with 25 nurses from a ward that had introduced an SPCS and 57 nurses from wards that used the traditional patient care system (TPCS). The major advantages of the SPCS compared to the TPCS include the specification of alarm purposes, the routing of alarms directly to the mobile phone; the capability of immediate communication via phone; and three-stage bed-exit alerts with low false alarm rate.

Results

Approximately 56% of nurses in the TPCS wards perceived that the bed-exit alert was easily ignorable, while this rate was reduced to 32% in the SPCS ward. The immediate communication via phone was considered as the most helpful function of the SPCS, with a weighted average score of 3.92/5, and 52% of nurses strongly agreed (5/5) that this function was helpful. The second-highest ranked function was the three-stage bed-exit alert, with an average score of 3.68/5, with approximately 24% of nurses strongly agreeing (5/5) that this function was helpful. The average response time using TPCS was 145.66 s while it was 59.02 s using the SPCS (P < .001). Among the 110 observed alarms in the SPCS ward, none of them were false bed-exit alarms. In comparison, among 120 observed alarms in the TPCS wards, 42 (35%) of them were false bed-exit alarms (P < .001). In this study, we found that 30.91% of alarms using SPCS were processed because nurses received and responded to the alert via mobile phone.

Conclusions

A smart patient care system is needed to help nurses make more informed prioritization decisions between responding to alarms and ongoing tasks and finally assist them in adjusting their work in various situations to improve work efficiency and care quality.

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

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

Wandering is a common behavioral disorder in the community-dwelling elderly. More than two-thirds of caregivers believe that wandering would cause falls. While physical restraint is a common measure to address wandering, it could trigger challenging behavior in approximately 80% of the elderly with dementia. This study aims to develop a virtual restraint using a night monitoring system (eNightLog) to provide a safe environment for the elderly and mitigate the caregiver burden. The eNightLog system consisted of remote sensors, including a near infra-red 3D time-of-flight sensor and ultrawideband sensors. An alarm system was controlled by customized software and algorithm based on the respiration rate and body posture of the elderly. The performance of the eNightLog system was evaluated in both single and double bed settings by comparing to that of a pressure mat and an infrared fence system, under simulated bed-exiting scenarios. The accuracy and precision for the three systems were 99.0%, 98.8%, 85.9% and 99.2%, 97.8%, 78.6%, respectively. With higher accuracy, precision, and a lower false alarm rate, eNightLog demonstrated its potential as an alternative to physical restraint to remedy the workload of the caregivers and the psychological impact of the elderly.

Alarming and/or Alerting Device Effectiveness in Reducing Falls in Long-Term Care (LTC) Facilities? A Systematic Review

Perceptions against the use of alarming devices persist in long-term care environments as they are seen as annoying, costly, and a waste of time to the staff involved. Ascertaining whether these perceptions are true or false via the literature was a focus of this study. Proper information to educate staff and to work past these perceptions can be a positive effector for resident safety. Many facilitators for the use of alarming devices were found, as well as many barriers to their use as well. New technology is changing the perceptions regarding these types of devices as time passes. Education is a key component for staff, residents, and families. There are “traditional” issues with the use of alarms such as alarm fatigue by caregivers, high costs of implementation, and issues with proper implementation of alarms. Alarms are perceived as intrusive and the noise from them can be a potential cause of falls. However, alarming devices can be a key intervention in the safety of those residents who are prone to falls. This requires proper implementation and education for all parties involved, and proper oversight surrounding use of the devices.

INBED: A Highly Specialized System for Bed-Exit-Detection and Fall Prevention on a Geriatric Ward

OBJECTIVE

In geriatric institutions, the risk of falling of patients is very high and frequently leads to fractures of the femoral neck, which can result in serious consequences and medical costs. With regard to the current numbers of elderly people, the need for smart solutions for the prevention of falls in clinical environments as well as in everyday life has been evolving.

METHODS

Hence, in this paper, we present the Inexpensive Node for bed-exit Detection (INBED), a comprehensive, favourable signaling system for bed-exit detection and fall prevention, to support the clinical efforts in terms of fall reduction. The tough requirements for such a system in clinical environments were gathered in close cooperation with geriatricians.

RESULTS

The conceptional efforts led to a multi-component system with a core wearable device, attached to the patients, to detect several types of movements such as rising, restlessness and-in the worst case-falling. Occurring events are forwarded to the nursing staff immediately by using a modular, self-organizing and dependable wireless infrastructure. Both, the hardware and software of the entire INBED system as well as the particular design process are discussed in detail. Moreover, a trail test of the system is presented.

CONCLUSIONS

The INBED system can help to relieve the nursing staff significantly while the personal freedom of movement and the privacy of patients is increased compared to similar systems.

Noncontact In-Bed Measurements of Physiological and Behavioral Signals Using an Integrated Fabric-Sheet Sensing Scheme

Home monitoring requires measuring the physiological and behavioral signals without impairing a subject's everyday life. This paper presents an integrated and noncontact approach for obtaining simultaneous physiological and behavioral signals of recumbent humans in beds using a home-monitoring application. In the proposed approach, a fabric-sheet unified sensing electrode (FUSE) obtains physiological signals by recording the electrocardiogram (ECG), chest and abdominal respiratory movements (RMs), and ballistocardiogram (BCG). The FUSE also detects the behavioral signals of body proximity (BPx) and lateral/supine lying postures. A prototype system with FUSE was validated in a short-term experiment and 6-h overnight measurements on two different groups composed of seven lying subjects. The results confirmed that the approach senses each signal independently and records the ECG, RMs, BCG, and BPx signals simultaneously. The mean sensitivities of the R and T waves of the ECG during sleep were 86.1% and 88.0%, respectively, whereas those of the chest and abdominal RMs were 90.7% and 90.1%, respectively. Although our prototype system has room for improvement, the results suggest that our approach enables the unconstrained, nocturnal monitoring of the physiological and behavioral signals in recumbent humans. The at-home monitoring of the physiological and behavioral signals is expected to contribute to cost-effective personalized healthcare in the future. This noncontact and easy-to-install system for in-bed measurements can facilitate a new era of home monitoring.

A Novel Algorithm for Determining the Contextual Characteristics of Movement Behaviors by Combining Accelerometer Features and Wireless Beacons: Development and Implementation

BACKGROUND

Unfortunately, global efforts to promote "how much" physical activity people should be undertaking have been largely unsuccessful. Given the difficulty of achieving a sustained lifestyle behavior change, many scientists are reexamining their approaches. One such approach is to focus on understanding the context of the lifestyle behavior (ie, where, when, and with whom) with a view to identifying promising intervention targets.

OBJECTIVE

The aim of this study was to develop and implement an innovative algorithm to determine "where" physical activity occurs using proximity sensors coupled with a widely used physical activity monitor.

METHODS

A total of 19 Bluetooth beacons were placed in fixed locations within a multilevel, mixed-use building. In addition, 4 receiver-mode sensors were fitted to the wrists of a roving technician who moved throughout the building. The experiment was divided into 4 trials with different walking speeds and dwelling times. The data were analyzed using an original and innovative algorithm based on graph generation and Bayesian filters.

RESULTS

Linear regression models revealed significant correlations between beacon-derived location and ground-truth tracking time, with intraclass correlations suggesting a high goodness of fit (R²=.9780). The algorithm reliably predicted indoor location, and the robustness of the algorithm improved with a longer dwelling time (>100 s; error <10%, R²=.9775). Increased error was observed for transitions between areas due to the device sampling rate, currently limited to 0.1 Hz by the manufacturer.

CONCLUSIONS

This study shows that our algorithm can accurately predict the location of an individual within an indoor environment. This novel implementation of "context sensing" will facilitate a wealth of new research questions on promoting healthy behavior change, the optimization of patient care, and efficient health care planning (eg, patient-clinician flow, patient-clinician interaction).

Assessing the use of Radio Frequency Identification technologies as an alternative for insurance costs in hospitals

OBJECTIVES

This research assesses the use of Radio Frequency Identification (RFID) technologies as an alternative for insurance costs in hospitals.

METHODS

Despite the advantages of RFID, this technology has not been applied in most hospitals due to implementation costs and amortization of RFID. In this paper, we intend to model the total profit of hospitals in three scenarios namely, application of RFID technology in the hospital, without applying RFID technology in the hospital and insuring patients and equipment in the hospital.

RESULTS

We analyzed the aforementioned situations over a period of time to find out how they affect the profit of the hospital. Based on this analysis we concluded that if applying RFID technology is costly, it will be feasible for advanced hospitals with more beds. In the scenario of insuring patients and equipment, if insurance organization takes over a small portion of the cost of the mistakes and oversights, insuring patients and equipment will not be feasible for the hospital, and it is better to apply RFID technology Instead.

CONCLUSIONS

RFID is among the technologies applied to reduce mistakes of the personnel in hospitals. Moreover, applying this technology has led to a decrease in the number of personnel required in hospitals. This study models total profit of hospitals in three aforementioned scenarios. Based on analyzing these models we conclude that if applying RFID technology is costly, it will be feasible for advanced hospitals with more beds.

A battery-less and wireless wearable sensor system for identifying bed and chair exits in a pilot trial in hospitalized older people

Falls in hospitals are common, therefore strategies to minimize the impact of these events in older patients and needs to be examined. In this pilot study, we investigate a movement monitoring sensor system for identifying bed and chair exits using a wireless wearable sensor worn by hospitalized older patients. We developed a movement monitoring sensor system that recognizes bed and chair exits. The system consists of a machine learning based activity classifier and a bed and chair exit recognition process based on an activity score function. Twenty-six patients, aged 71 to 93 years old, hospitalized in the Geriatric Evaluation and Management Unit participated in the supervised trials. They wore over their attire a battery-less, lightweight and wireless sensor and performed scripted activities such as getting off the bed and chair. We investigated the system performance in recognizing bed and chair exits in hospital rooms where RFID antennas and readers were in place. The system’s acceptability was measured using two surveys with 0–10 likert scales. The first survey measured the change in user perception of the system before and after a trial; the second survey, conducted only at the end of each trial, measured user acceptance of the system based on a multifactor sensor acceptance model. The performance of the system indicated an overall recall of 81.4%, precision of 66.8% and F-score of 72.4% for joint bed and chair exit recognition. Patients demonstrated improved perception of the system after use with overall score change from 7.8 to 9.0 and high acceptance of the system with score ≥ 6.7 for all acceptance factors. The present pilot study suggests the use of wireless wearable sensors is feasible for detecting bed and chair exits in a hospital environment.

Recognizing Bedside Events Using Thermal and Ultrasonic Readings

Falls in homes of the elderly, in residential care facilities and in hospitals commonly occur in close proximity to the bed. Most approaches for recognizing falls use cameras, which challenge privacy, or sensor devices attached to the bed or the body to recognize bedside events and bedside falls. We use data collected from a ceiling mounted 80 × 60 thermal array combined with an ultrasonic sensor device. This approach makes it possible to monitor activity while preserving privacy in a non-intrusive manner. We evaluate three different approaches towards recognizing location and posture of an individual. Bedside events are recognized using a 10-second floating image rule/filter-based approach, recognizing bedside falls with 98.62% accuracy. Bed-entry and exit events are recognized with 98.66% and 96.73% accuracy, respectively.

Application of Deep Learning to Develop a Safety Confirmation System for the Elderly in a Nursing Home

[abstFig src='/00290002/07.jpg' width='210' text='The motion detection system' ] Fall accident prevention is one of the most important issues in elderly care settings. To prevent an accident, it is necessary to notify caregivers if the elderly person is getting out of bed. We have previously developed a posture discrimination system based on body motions. Herein, we propose a discrimination method by using machine learning to improve the performance of the system. A purpose of this study is to evaluate the proposed method. Elderly people in a nursing home were chosen as subjects in this study. We analyzed the body motion data during bed rest and bed exit of the subjects using the proposed method. These results suggest that it is effective.

Novel technology to help understand the context of physical activity and sedentary behaviour

When used in large, national surveillance programmes, objective measurement tools provide prevalence estimates of low physical activity guideline compliance and high amounts of sedentary time. There are undoubtedly a plethora of reasons for this but one possible contributing factor is the current lack of behavioural context offered by accelerometers and posture sensors. Context includes information such as where the behaviour occurs, the type of activity being performed and is vital in allowing greater refinement of intervention strategies. Novel technologies are emerging with the potential to provide this information. Example data from three ongoing studies is used to illustrate the utility of these technologies. Study one assesses the concurrent validity of electrical energy monitoring and wearable cameras as measures of television viewing. This study found that on average the television is switched on for 202 min d^-1 but is visible in just 90 min of wearable camera images with a further 52 min where the participant is in their living room but the television is not visible in the image. Study two utilises indoor location monitoring to assess where older adult care home residents accumulate their sedentary time. This study found that residents were highly sedentary (sitting for an average of 720 min d^-1) and spent the majority of their time in their own rooms with more time spent in communal areas in the morning than in the afternoon. Lastly, study three discusses the use of proximity sensors to quantify exposure to a height adjustable desk. These studies are example applications of this technology, with many other technologies available and applications possible. The adoption of these technologies will provide researchers with a more complete understanding of the behaviour than has previously been available.

Effectiveness of a Batteryless and Wireless Wearable Sensor System for Identifying Bed and Chair Exits in Healthy Older People

Aging populations are increasing worldwide and strategies to minimize the impact of falls on older people need to be examined. Falls in hospitals are common and current hospital technological implementations use localized sensors on beds and chairs to alert caregivers of unsupervised patient ambulations; however, such systems have high false alarm rates. We investigate the recognition of bed and chair exits in real-time using a wireless wearable sensor worn by healthy older volunteers. Fourteen healthy older participants joined in supervised trials. They wore a batteryless, lightweight and wireless sensor over their attire and performed a set of broadly scripted activities. We developed a movement monitoring approach for the recognition of bed and chair exits based on a machine learning activity predictor. We investigated the effectiveness of our approach in generating bed and chair exit alerts in two possible clinical deployments (Room 1 and Room 2). The system obtained recall results above 93% (Room 2) and 94% (Room 1) for bed and chair exits, respectively. Precision was >78% and 67%, respectively, while F-score was >84% and 77% for bed and chair exits, respectively. This system has potential for real-time monitoring but further research in the final target population of older people is necessary.

6-PACK programme to decrease fall injuries in acute hospitals: cluster randomised controlled trial

OBJECTIVE

To evaluate the effect of the 6-PACK programme on falls and fall injuries in acute wards.

DESIGN

Cluster randomised controlled trial.

SETTING

Six Australian hospitals.

PARTICIPANTS

All patients admitted to 24 acute wards during the trial period.

INTERVENTIONS

Participating wards were randomly assigned to receive either the nurse led 6-PACK programme or usual care over 12 months. The 6-PACK programme included a fall risk tool and individualised use of one or more of six interventions: "falls alert" sign, supervision of patients in the bathroom, ensuring patients' walking aids are within reach, a toileting regimen, use of a low-low bed, and use of a bed/chair alarm.

MAIN OUTCOME MEASURES

The co-primary outcomes were falls and fall injuries per 1000 occupied bed days.

RESULTS

During the trial, 46 245 admissions to 16 medical and eight surgical wards occurred. As many people were admitted more than once, this represented 31 411 individual patients. Patients' characteristics and length of stay were similar for intervention and control wards. Use of 6-PACK programme components was higher on intervention wards than on control wards (incidence rate ratio 3.05, 95% confidence interval 2.14 to 4.34; P<0.001). In all, 1831 falls and 613 fall injuries occurred, and the rates of falls (incidence rate ratio 1.04, 0.78 to 1.37; P=0.796) and fall injuries (0.96, 0.72 to 1.27; P=0.766) were similar in intervention and control wards.

CONCLUSIONS

Positive changes in falls prevention practice occurred following the introduction of the 6-PACK programme. However, no difference was seen in falls or fall injuries between groups. High quality evidence showing the effectiveness of falls prevention interventions in acute wards remains absent. Novel solutions to the problem of in-hospital falls are urgently needed.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry ACTRN12611000332921.

Measurement of bed turning and comparison with age, gender, and body mass index in a healthy population: application of a novel mobility detection system

We developed a mobility detection system to analyze pressure changes over time during side-turns in 29 healthy volunteers (17 males and 12 females) with a mean age of 46.1 ± 19.64 years (ranging from 23 to 86 years) in order to determine the effect of gender, age, and BMI on performance during bed postural change. Center of gravity (COG) location, peak pressure of counteraction, and time to reach peak pressure were the main outcomes used to gauge the ability to make a spontaneous side-turn. Men exhibited significantly higher side-turning force (P = 0.002) and back-turning force (P = 0.002) compared with women. Subjects with BMI ≥27 kg/m² had significantly higher side-turning force (P = 0.007) and back-turning force (P = 0.007) compared with those with BMI < 27 kg/m². After adjusting for other covariates, age positively correlated with back-turning time (P = 0.033) and negatively correlated with side-turning speed (P = 0.005), back-turning speed (P = 0.014), side-turning force (P = 0.010), and back-turning force (P = 0.016), respectively. Turning times negatively correlated with time to reach peak pressure (P = 0.008). Our system was effective in detecting changes in turning swiftness in the bed-ridden subject.