Use of Wearable Sensors and Machine Learning Methods in Promoting Total Joint Replacement Treatment Outcomes: A Survey

2020 ◽  
Vol 64 (1) ◽  
pp. 627-631
Author(s):  
Mustafa Ozkan Yerebakan ◽  
Xiang Zhong ◽  
Hari K. Parvataneni ◽  
Chancellor F. Gray ◽  
Boyi Hu
Keyword(s):  
Machine Learning ◽  
Joint Replacement ◽  
Total Joint Replacement ◽  
Population Aging ◽  
Wearable Sensors ◽  
The Elderly ◽  
World Population ◽  
Replacement Treatment ◽  
Time Burden ◽  
Prediction Evaluation

Total Joint Replacement (TJR) surgeries are one of the most prevalent operations that are undergone by the elderly population. With the world population aging, the number of surgeries will continue to increase. A small portion of these surgeries result in complications that require readmissions. These readmissions amount to a significant financial and time burden for both the patients and the hospitals. In the past decade machine learning and wearable sensors have both been used extensively in the healthcare domain but the contribution to the prediction/evaluation and management of TJR is limited. What’s more, to our best knowledge there has been no effort in summarizing the findings from these studies. Therefore, this study highlights what has been achieved by using machine learning and wearable sensors in the TJR context and point out possible research avenues.

scholarly journals Machine Learning based Human Fall Detection System

2021 ◽  
Vol 9 (VI) ◽  
pp. 2677-2682
Author(s):  
Nishanth P
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Wearable Sensors ◽  
Fall Detection ◽  
The Elderly ◽  
World Health ◽  
Support Vector ◽  
Data Output ◽  
Health Organization ◽  
Context Features

Falls have become one of the reasons for death. It is common among the elderly. According to World Health Organization (WHO), 3 out of 10 living alone elderly people of age 65 and more tend to fall. This rate may get higher in the upcoming years. In recent years, the safety of elderly residents alone has received increased attention in a number of countries. The fall detection system based on the wearable sensors has made its debut in response to the early indicator of detecting the fall and the usage of the IoT technology, but it has some drawbacks, including high infiltration, low accuracy, poor reliability. This work describes a fall detection that does not reliant on wearable sensors and is related on machine learning and image analysing in Python. The camera's high-frequency pictures are sent to the network, which uses the Convolutional Neural Network technique to identify the main points of the human. The Support Vector Machine technique uses the data output from the feature extraction to classify the fall. Relatives will be notified via mobile message. Rather than modelling individual activities, we use both motion and context information to recognize activities in a scene. This is based on the notion that actions that are spatially and temporally connected rarely occur alone and might serve as background for one another. We propose a hierarchical representation of action segments and activities using a two-layer random field model. The model allows for the simultaneous integration of motion and a variety of context features at multiple levels, as well as the automatic learning of statistics that represent the patterns of the features.

scholarly journals A Novel Elderly Tracking System Using Machine Learning to Classify Signals from Mobile and Wearable Sensors

2021 ◽  
Vol 18 (23) ◽  
pp. 12652
Author(s):  
Jirapond Muangprathub ◽  
Anirut Sriwichian ◽  
Apirat Wanichsombat ◽  
Siriwan Kajornkasirat ◽  
Pichetwut Nillaor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Monitoring System ◽  
Nearest Neighbor ◽  
Personal Information ◽  
Tracking System ◽  
Elderly Care ◽  
Wearable Sensors ◽  
The Elderly ◽  
Outdoor Environment ◽  
K Nearest Neighbor

A health or activity monitoring system is the most promising approach to assisting the elderly in their daily lives. The increase in the elderly population has increased the demand for health services so that the existing monitoring system is no longer able to meet the needs of sufficient care for the elderly. This paper proposes the development of an elderly tracking system using the integration of multiple technologies combined with machine learning to obtain a new elderly tracking system that covers aspects of activity tracking, geolocation, and personal information in an indoor and an outdoor environment. It also includes information and results from the collaboration of local agencies during the planning and development of the system. The results from testing devices and systems in a case study show that the k-nearest neighbor (k-NN) model with k = 5 was the most effective in classifying the nine activities of the elderly, with 96.40% accuracy. The developed system can monitor the elderly in real-time and can provide alerts. Furthermore, the system can display information of the elderly in a spatial format, and the elderly can use a messaging device to request help in an emergency. Our system supports elderly care with data collection, tracking and monitoring, and notification, as well as by providing supporting information to agencies relevant in elderly care.

scholarly journals Smart Nursing Homes: Self-Management Architecture Based on IoT and Machine Learning for Rural Areas

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Daniel Flores-Martin ◽  
Javier Rojo ◽  
Enrique Moguel ◽  
Javier Berrocal ◽  
Juan M. Murillo
Keyword(s):  
Quality Of Life ◽  
Nursing Homes ◽  
Elderly People ◽  
Rural Areas ◽  
Population Aging ◽  
The Elderly ◽  
Smart Devices ◽  
World Population ◽  
Use Of Technology

The rate of world population aging is increasing. This situation directly affects all countries socially and economically, increasing their compromise and effort to improve the living conditions of this sector of society. In environments with large influxes of elderly people, such as nursing homes, the use of technology has shown promise in improving their quality of life. The use of smart devices allows people to automate everyday tasks and learn from them to predict future actions. Additionally, smartphones capture a wealth of information that allows to adapt to nearby actuators according to people’s preferences and even detects anomalies in their behaviour. Current works are proposing new frameworks to detect these behaviours and act accordingly. However, these works are not focused on managing multidevice environments where sensor and smartphone data are considered to automate environments with elderly people or to learn from them. Also, most of these works require a permanent Internet connection, so the full benefit of smart devices is not completely achieved. In this work, we present an architecture that takes the data from sensors and smartphones in order to adapt the behaviour of the actuators of the environment. In addition, it uses this data to learn from the environment to predict actions or to extrapolate the actions that should be executed according to similar behaviours. The architecture is implemented through a use case based on a nursing home located in a rural area. Thanks to this work, the quality of life of the elderly is improved in a simple, affordable, and transparent way for them.

scholarly journals Recognition of Sedentary Behavior by Machine Learning Analysis of Wearable Sensors during Activities of Daily Living for Telemedical Assessment of Cardiovascular Risk

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3219 ◽  
Author(s):  
Eliasz Kańtoch
Keyword(s):  
Machine Learning ◽  
Cardiovascular Risk ◽  
Sedentary Behavior ◽  
Behavior Pattern ◽  
Wearable Sensors ◽  
The Elderly ◽  
Living Environment ◽  
Support Vector ◽  
Accelerometer Data ◽  
Linear Discriminant

With the recent advancement in wearable computing, sensor technologies, and data processing approaches, it is possible to develop smart clothing that integrates sensors into garments. The main objective of this study was to develop the method of automatic recognition of sedentary behavior related to cardiovascular risk based on quantitative measurement of physical activity. The solution is based on the designed prototype of the smart shirt equipped with a processor, wearable sensors, power supply and telemedical interface. The data derived from wearable sensors were used to create feature vector that consisted of the estimation of the user-specific relative intensity and the variance of filtered accelerometer data. The method was validated using an experimental protocol which was designed to be safe for the elderly and was based on clinically validated short physical performance battery (SPPB) test tasks. To obtain the recognition model six classifiers were examined and compared including Linear Discriminant Analysis, Support Vector Machines, K-Nearest Neighbors, Naive Bayes, Binary Decision Trees and Artificial Neural Networks. The classification models were able to identify the sedentary behavior with an accuracy of 95.00% ± 2.11%. Experimental results suggested that high accuracy can be obtained by estimating sedentary behavior pattern using the smart shirt and machine learning approach. The main advantage of the developed method to continuously monitor patient activities in a free-living environment and could potentially be used for early detection of increased cardiovascular risk.

Big Data, Machine Learning, and Health Knowledge Discovery in the Elderly in China

2020 ◽  
pp. 29-52
Author(s):  
Bin Ding ◽  
Dongxiao Gu ◽  
Zheng Jiang
Keyword(s):  
Machine Learning ◽  
Life Satisfaction ◽  
Health Status ◽  
Healthy Aging ◽  
Health Knowledge ◽  
Pension System ◽  
Population Aging ◽  
The Elderly ◽  
Predicting Factors ◽  
Healthy Longevity

According to the national strategic plan for healthy aging and the construction of the pension system in China, it is expected that by 2020 the population of elderly aged 60 and above will reach 255 million, accounting for about 17.8% of the total population. Currently, population aging is a serious social problem in China, and thus, health status of the elderly becomes increasingly critical. The present research uses machine learning to identify factors influencing elderly's health status and life satisfaction with data from the Chinese Longitudinal Healthy Longevity Survey. The results show that some common factors are important for both self-rated health status and life satisfaction for elderly, namely positive and optimistic attitudes, a healthy diet, and economic status. Health status and life satisfaction also have their unique predicting factors, such as mobility ability for health status and living conditions for life satisfaction. Theoretical and practical implications of the findings are discussed.

Total Joint Replacement in the Elderly Patient

2010 ◽  
Vol 26 (3) ◽  
pp. 517-529 ◽  
Author(s):  
Carl T. Talmo ◽  
Claire E. Robbins ◽  
James V. Bono
Keyword(s):  
Elderly Patient ◽  
Joint Replacement ◽  
Total Joint Replacement ◽  
The Elderly

Forms of physical activity of the elderly

2020 ◽  
Vol 18 (3) ◽  
pp. 221-228
Author(s):  
Piotr Czarnecki ◽  
◽  
Justyna Podgórska-Bednarz ◽  
Lidia Perenc ◽  
◽  
...  
Keyword(s):  
Physical Activity ◽  
Elderly People ◽  
Population Aging ◽  
Human Life ◽  
Good Health ◽  
The Elderly ◽  
Polish Society ◽  
Central Statistical ◽  
Key Phrases ◽  
The Impact

Introduction. Physical activity is known to be an important factor influencing health throughout human life. This issue has become crucial for public health due to the aging of the population in both developed and developing countries. Aim. is to present a literature review on the forms of physical activity undertaken by the elderly, as well as on issues related to physical activity and the population aging. Material and methods. The study was prepared on the basis of a review of Polish and foreign literature. The following databases and data sources were used: EBSCO, ScienceDirect and Google Scholar. An additional source of data were the websites of the Central Statistical Office. Strictly defined key phrases were used during the collection of literature. The work has been divided into thematic subsections on the aging of the society, the impact of physical activity on health and the main topic, i.e. forms of physical activity selected by the elderly. Analysis of the literature. The number of elderly people in Polish society has increased by almost 3.7 million over three decades. Therefore, an important topic is prophylaxis aimed at increasing the number of days in good health, largely covering the broadly understood activation of the elderly. The available data indicate that only 12% of elderly people undertake physical activity once a week. The most common form of spending free time actively is walking (as many as 73% of people in this population declare this form of physical activity in one of the presented studies). Conclusion. Organized forms of physical activity are undertaken much less frequently by the analyzed age group mainly due to financial limitations and limited availability of sports infrastructure.

Piroxicam spares buprenorphine after total joint replacement

1992 ◽  
Vol 63 (6) ◽  
pp. 658-660
Author(s):  
Michel Boeckstyns ◽  
Marianne Backer ◽  
Else Petersen ◽  
Iben Høj ◽  
Henrik Albrechtsen ◽  
...  
Keyword(s):  
Joint Replacement ◽  
Total Joint Replacement

Postoperative Anesthesia Of Elderly And Senile Patients With Total Joint Replacement Of The Lower Extremities

2020 ◽  
Vol 2 (09) ◽  
pp. 75-81
Author(s):  
Gaziev Z.T. ◽  
Avakov V.E. ◽  
Shorustamov M.T. ◽  
Bektemirova N.T.
Keyword(s):  
Epidural Analgesia ◽  
Postoperative Period ◽  
Joint Replacement ◽  
Total Joint Replacement ◽  
Lower Extremities ◽  
Multimodal Analgesia ◽  
Control Group ◽  
Patient Controlled Analgesia ◽  
Physical Status ◽  
Traumatic Injuries

Objective: To evaluate the efficacy and safety of patient-controlled analgesia through prolonged epidural analgesia after joint replacement of the lower extremities. Material and methods. We analyzed the postoperative period of 213 elderly and senile patients who were operated on for degenerative-dystrophic and traumatic injuries of the joints of the lower extremities. All patients underwent total joint replacement (164 - THA and 49 - TKA). The age of patients is from 65 to 90 years (average age was 78 ± 8 years) with a physical status of ASA 3 and above. All examined patients were divided into 2 groups. 63 patients comprised the main group, which in the postoperative period underwent patient-controlled analgesia (PCA) through prolonged epidural analgesia. The control group consisted of 150 patients, for the anesthesia of which in the postoperative period only standard systemic multimodal analgesia was used Conclusion. Patient-controlled analgesia is an alternative to traditional analgesic regimens. This method should be one of the main methods after surgical anesthesia for joint replacement of the lower limb in elderly and senile patients.

Sign in / Sign up

Export Citation Format

Share Document