Smart postural monitor for elderly people (original) (raw)
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Sensors, 2018
Monitoring the posture of older persons using portable sensors while they carry out daily activities can facilitate the process of generating indicators with which to evaluate their health and quality of life. The majority of current research into such sensors focuses primarily on their functionality and accuracy, and minimal effort is dedicated to understanding the experience of older persons who interact with the devices. This study proposes a wearable device to identify the bodily postures of older persons, while also looking into the perceptions of the users. For the purposes of this study, thirty independent and semi-independent older persons undertook eight different types of physical activity, including: walking, raising arms, lowering arms, leaning forward, sitting, sitting upright, transitioning from standing to sitting, and transitioning from sitting to standing. The data was classified offline, achieving an accuracy of 93.5%, while overall device user perception was positive. Participants rated the usability of the device, in addition to their overall user experience, highly.
SIT LESS: A prototype home-based system for monitoring older adults sedentary behavior
Assistive Technology, 2018
This paper presents the overall design of a prototype home-based system aimed to reduce sedentary behavior of older adults. Quantitative performance indicators were developed to measure the sedentary behavior and daily activities of an older adult. The sedentary behavior is monitored by identifying individual positions (standing, sitting, and lying) within the field of view of a Microsoft Kinect sensor, using a custom designed algorithm. The physical activity of the older adult when outside the field of view of the Microsoft Kinect sensor, is monitored by counting the number of steps using a Fitbit Charge HR watch, which the older adult is equipped with. A user interface was developed on a PC platform to interact with the older adult. The user A c c e p t e d M a n u s c r i p t 2 interface is automatically operated and includes several modules. It displays the activity level, and provides feedbacks, alerts, and reminders to reduce sedentary behavior. Evaluations using a mixed methods approach that included a focus group, interviews, and observations were conducted to examine the integrated system, evaluate the users' experience with the system, and compare different types of feedbacks and alerts. The analyses indicated the feasibility of the proposed SIT LESS system along with recommendations for improving the system in future research.
IoT Based Smart Posture Detector
Advances in Intelligent Systems and Computing
The growing technology in the world is rapidly transforming the way people lead their lives. Industrialization and urbanization have brought an enormous increase in sedentary lifestyle to the modern world. Indulged in technology, people are often found abandoning their good posture and being hunched over for really long hours. Good posture is of utmost importance for leading a healthy lifestyle and it is said that back pain is the third most common reason for people to visit the doctor. Yet, knowingly or unknowingly, people compromise on one of the most essential traits of what makes them human; the ability to walk upright. The aim of this paper is to provide a feasible solution to this problem by presenting a wearable device that recognizes the posture of the person and sends live data on the phone through an app. It records the posture and classifies it as Good, Okay or Bad. It also gives the statistics and overall feedback of how it can be improved.
Sensors
In contrast to the physical activities of able-bodied people at home, most people who require long-term specific care (e.g., bedridden patients and patients who have difficulty walking) usually show more low-intensity slow physical activities with postural changes. Although the existing devices can detect data such as heart rate and the number of steps, they have been increasing the physical burden relying on long-term wearing. The purpose of this paper is to realize a noninvasive fine-grained home care monitoring system that is sustainable for people requiring special care. In the proposed method, we present a novel technique that integrates inexpensive camera devices and bone-based human sensing technologies to characterize the quality of in-home postural changes. We realize a local process in feature data acquisition once per second, which extends from a computer browser to Raspberry Pi. Our key idea is to regard the changes of the bounding box output by standalone pose estimatio...
A novel technique for analysis of postural information with wearable devices
2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2018
These days, as many jobs involve sitting behind desks and working with computers for extended periods, more and more people are suffering from back problems. Maintenance of an appropriate posture may prevent future back problems. There are various medical methods for studying postures abnormalities of the back but most of these methods are limited to be utilized in diagnostics and follow-up of treatment and not used in a continuous or in a preventive manner. Therefore, designing and developing methods for measuring, analyzing and reporting of posture information, aimed for prevention of future back problems is of fundamental interest. In this work, a proof-of-concept system, including five accelerometer sensor units is presented. Additionally, an index, which we call spine inclination index (SII), is introduced and used for converting the raw data to meaningful presentable information. Initial evaluation includes measurements with six subjects. Subjects were asked to mimic accentuated kyphotic, straight and accentuated lordotic postures while sitting. Our results show that the designed device and SII index are able to distinguish between different postures very well. In addition, since this device measures the inclination angle of different spinal postures, its output can be directly compared with other widely used methods.
Activity Level Assessment Using a Smart Cushion for People with a Sedentary Lifestyle
Sensors (Basel, Switzerland), 2017
As a sedentary lifestyle leads to numerous health problems, it is important to keep constant motivation for a more active lifestyle. A large majority of the worldwide population, such as office workers, long journey vehicle drivers and wheelchair users, spends several hours every day in sedentary activities. The postures that sedentary lifestyle users assume during daily activities hide valuable information that can reveal their wellness and general health condition. Aiming at mining such underlying information, we developed a cushion-based system to assess their activity levels and recognize the activity from the information hidden in sitting postures. By placing the smart cushion on the chair, we can monitor users' postures and body swings, using the sensors deployed in the cushion. Specifically, we construct a body posture analysis model to recognize sitting behaviors. In addition, we provided a smart cushion that effectively combine pressure and inertial sensors. Finally, we...
Estimation of posture and prediction of the elderly getting out of bed using a body pressure sensor
International Journal of Electrical and Computer Engineering (IJECE), 2021
We propose an IoT support system for estimating the posture of the care recipient on the bed from the body pressure of the care recipient measured by a sheet-type body pressure sensor, and detecting the posture related to leaving the bed in real time. In addition, we propose a method that predicts getting out of the bed before the care recipient takes a posture related to getting out of the bed by considering the state transition. Intervention experiment showed that using body pressure features as an explanatory variable and applying machine learning, 16 types of postures on the bed of care recipients with an F value of 0.7 or more could be identified. From the experiment without intervention, by applying the hidden Markov model, we calculated the transition probability to each hidden state when the care recipient getting out of the bed and the transition probability to each hidden state when the care recipient not getting out of the bed. As a result, there was a difference of about 0.1 in the transition probability of the state related to raising upper body. Keywords: Elderly care Hidden markov model Machine learning Pressure sensor Sensing This is an open access article under the CC BY-SA license.
A Review on posture monitoring systems
2018 International Conference on Smart Communications and Networking (SmartNets), 2018
The bad sitting posture leads to many back problems for young people. Nowadays the adults and young people seat for long hours hunching over their laptops and tablets. Recent researches are focus on the study and develop systems to prevent the seated persons from spinal pain with monitoring and improve the sitting posture in real-time. In this study we analyze the specification of sitting posture monitoring systems, the information required to define the human posture, the technologies used and the systems limits. The person posture can be identified by different information provided by the sensing technologies. This research can be a reference for future study for posture monitoring systems.
Development of an Inexpensive Sensor Network for Recognition of Sitting Posture
International Journal of Distributed Sensor Networks, 2015
The aim of this work is the development of a network of wireless devices to determine, along with a time-stamp, postural changes of users that are to be used in personalized learning environments. For this purpose, we have designed a basic low-cost pressure sensor that can be built from components easily available. Several of these basic sensors (of sizes and shapes chosen specifically for the task) are integrated into a posture sensor cushion, which is electronically controlled by an Arduino microcontroller board. This accounts for experiments involving either a single cushion to be used by an individual end-user setting approach or classroom approaches where several of these cushions make up a sensor network via ZigBee wireless connections. The system thus formed is an excellent alternative to other more expensive commercial systems and provides a low-cost, easy-to-use, portable, scalable, autonomous, flexible solution with free hardware and software, which can be integrated with ...