AI based elderly fall prediction system using wearable sensors: A smart home-care technology with IOT

Kulurkar, Pravin; Dixit, Chandra kumar; Bharathi, V.C.; Monikavishnuvarthini, A.; Dhakne, Amol; Preethi, P.

doi:10.1016/j.measen.2022.100614

Cited by 51 publications

(23 citation statements)

References 12 publications

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“…Mitigation strategies, such as space modifications and assist devices, are appropriate and often already used in HaH. However, RPM for near-fall or fall detection can be used to further patient safety through constant monitoring of patients at appropriate risk (acknowledging that high-risk patients are perhaps best served in traditional inpatient settings) [ 21 ]. In our experience at Massachusetts General Hospital, applying RPM fall monitoring capabilities on appropriate patients, we have found that it has increased our detection of falls.…”

Section: Opportunities For Remote Patient Monitoring For Hahmentioning

confidence: 99%

The Next Frontier of Remote Patient Monitoring: Hospital at Home

Whitehead¹,

Conley²

2023

J Med Internet Res

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Remote patient monitoring (RPM) has shown promise in aiding safe and efficient remote care for chronic conditions; however, its use remains more limited within the hospital at home (HaH) model of care despite a significant opportunity to increase patient eligibility, improve safety, and decrease costs. HaH could achieve these goals by further adopting the 3 primary modalities of RPM (ie, vital sign, continuous single-lead electrocardiogram, and fall monitoring). With only 2 in-person vital sign checks required per day, HaH patient eligibility is currently often limited to lower-acuity cases. The use of vital sign RPM within HaH could better match the standard clinical practice of vital sign checks every 4-8 hours and enable safe care for appropriate moderate-acuity medical and surgical floor-level patients not traditionally enrolled in HaH. Robust, efficient collection of more frequent vital signs via RPM could expand patient eligibility for HaH and create a digital health safety net that enables high quality care. Similarly, our experience at Massachusetts General Hospital has demonstrated that appropriate use of continuous single-lead electrocardiogram RPM can also expand HaH enrollment, particularly for patients with acute decompensated heart failure. Through increasing enrollment of patients in HaH, RPM stands to enable more patients to reap the potential safety benefits of home hospitalization, including decreased rates of delirium and hospital-acquired infections, and better avoid aspects of posthospital syndrome. Furthermore, instituting fall detection RPM allows care teams to further HaH patient safety during their episode of acute care and develop enhanced mitigation strategies to avoid falls post home hospitalization. RPM also has the potential to assist HaH in achieving greater economies of scale and decreasing direct variable costs. By expanding HaH eligibility, RPM could enable HaH programs, which have traditionally operated under capacity, to care for a larger census and decrease allocated fixed costs per hospitalization. Additionally, RPM for HaH could further optimize hybrid in-home and remote nurse or physician evaluations, decreasing costs on a per-episode basis by up to an estimated 3.5%. Overall, RPM holds great promise to increase patient eligibility and patient safety while decreasing costs. However, it is in its infancy in achieving its potential to advance the HaH model of care; further research and experience that inform operational and technical as well as policy considerations are needed.

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Section: Opportunities For Remote Patient Monitoring For Hahmentioning

confidence: 99%

The Next Frontier of Remote Patient Monitoring: Hospital at Home

Whitehead¹,

Conley²

2023

J Med Internet Res

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“…For example, it is very important to detect falls, which has already become a topic of particular importance in this field. For example, in [ 25 ], a framework was proposed for edge computing to detect individual’s falls using real-time monitoring by cost-effective wearable sensors. For this purpose, an IoT-based system that makes the use of big data, cloud computing, wireless sensor networks, and smart devices was developed and integrated with an LSTM model, showing very promising results for the detection of falls by elderly people in indoor circumstances.…”

Section: Related Workmentioning

confidence: 99%

Geriatric Care Management System Powered by the IoT and Computer Vision Techniques

Paulauskaitė-Tarasevičienė

Siaulys

Šutienė

et al. 2023

Healthcare

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The digitalisation of geriatric care refers to the use of emerging technologies to manage and provide person-centered care to the elderly by collecting patients’ data electronically and using them to streamline the care process, which improves the overall quality, accuracy, and efficiency of healthcare. In many countries, healthcare providers still rely on the manual measurement of bioparameters, inconsistent monitoring, and paper-based care plans to manage and deliver care to elderly patients. This can lead to a number of problems, including incomplete and inaccurate record-keeping, errors, and delays in identifying and resolving health problems. The purpose of this study is to develop a geriatric care management system that combines signals from various wearable sensors, noncontact measurement devices, and image recognition techniques to monitor and detect changes in the health status of a person. The system relies on deep learning algorithms and the Internet of Things (IoT) to identify the patient and their six most pertinent poses. In addition, the algorithm has been developed to monitor changes in the patient’s position over a longer period of time, which could be important for detecting health problems in a timely manner and taking appropriate measures. Finally, based on expert knowledge and a priori rules integrated in a decision tree-based model, the automated final decision on the status of nursing care plan is generated to support nursing staff.

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“…But if there is overcrowding on the source nodes, it will be difficult to accomplish this because it will lengthen the queue, which will add to the delay. Consequently, [21] suggested a MQAMs routing approach based on the MCDMs metric. Results reveal that MQAM routing is improved than MP-OLSR throughput.…”

Section: Related Workmentioning

confidence: 99%

“…When a packet arrives at an RREPs from the target address, the PSP value can be obtained eq (10) [21] this computes the LBPs values between both networks and the optimization algorithm of the link security Fs.…”

Section: Path Stability Probabilities (Psps)mentioning

confidence: 99%

Optimization of Manets Using Aomdv With Novel Aging Multi Population Strategy for Effectual Energy Utilization

R.S.¹

2023

Preprint

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In today’s world wireless technologies plays vital role for many real world applications particularly the Mobile Ad-hoc Network (MANET) with bidirectional transmission capacity through numerous intermediary nodes is having dynamic scope in near future. Whereas, packet collision is regarded as the most important restrictions in MANETs since nodes move randomly through the network at unpredictable speeds, increasing the likelihood of collision and degrading throughput, routing overhead, and end-to-end delay. Additionally, a topological shift and link instability caused by frequent node mobility lower the rate of data delivery. The probability of traffic crowding increases at the intermediary nodes due to limited possible routes to the destination network, which affects the successful delivery of packets, especially with real-world applications on MANETs. In the proposed work, a novel strategy of age to evaluate each particle's local area search capacity is anticipated with Aging Multi Population Optimization (AMPO). The particles are divided into distinct age groups according to their ages so that population variety can be maintained when searching. Particles within every group of age can only choose those in younger or from their own clusters / groups as preferred neighbors. To choose the optimum route to the destination, we optimise the many pathways that the Adhoc On-demand Multipath Distance Vector (AOMDV) mechanism returned. The most ideal route is thought to be the one with the highest value for fitness. In order to speed up convergence, we also create a parameter setting mechanism based on age groups, where particles in various age groups have distinct parameters. Finally, we evaluate our suggested approach in comparison to AOMDV-TA and EHO-AOMDV. For the performance assessment of the suggested model, network overhead, throughput, delay, energy usage, and delivery of packets range as vital aspects.

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AI based elderly fall prediction system using wearable sensors: A smart home-care technology with IOT

Cited by 51 publications

References 12 publications

The Next Frontier of Remote Patient Monitoring: Hospital at Home

The Next Frontier of Remote Patient Monitoring: Hospital at Home

Geriatric Care Management System Powered by the IoT and Computer Vision Techniques

Optimization of Manets Using Aomdv With Novel Aging Multi Population Strategy for Effectual Energy Utilization

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