Posture Detection System Research Articles

REALTIME IMPLEMENTATION OF SHOULDER POSTURE DETECTION WITH COMPUTER VISION

The development of health technology is currently growing rapidly along with the needs of health workers for medical information. Computer Vision is one of the technologies used in the medical world, including the human shoulder posture detection system using the MediaPipe library. This system automatically calculates the logic of posture measurements based on the basis of manual measurements with anthropometric tools, so that health workers can easily get precise and accurate information about the results of shoulder posture detection. The system created with the Python programming language allows real-time shoulder posture detection and avoids human error. This research successfully created a human shoulder posture detection system using MediaPipe on computer vision technology to make it easier for medical personnel to get real-time measurement results.

Using Machine-Learning Technology to Implement a Nonhardware and Inexpensive Posture Detection System for Analyzing Body Posture Angles in Front Crawl Swimming

<p>Swimming is a sport that relies heavily on motor skills. Inability to maintain adequate bodily balance in water prevents swimmers from remaining afloat and propelling themselves. Due to the difficulty of attaching reflective stickers or LED (light-emitting diode) emitters to the body while adjusting the swimming posture, it is not possible to capture the posture like during a bicycle fitting; the refraction of water also affects the detection of the body’s posture. Addressing the shortcomings, this study developed a low-cost nonhardware posture detection system based on machine-learning models in MediaPipe. The system provides real-time and post analyses of posture angles and posture lines during front crawl swimming, thereby facilitating observation of the relationship between angle at which the arm enters the water and the body horizon. Two participants practicing front crawl were invited to test the proposed system. The experimental results confirmed that the proposed system provides effective detection and analyses of posture lines and angles in swimmers. The study also proposed the algorithm for optimizing posture angle detection to solve the problems of posture line distortion and angle calculation errors that arise when MediaPipe was used to detect a human skeleton above a water line. The system does not require the installation of hardware and is inexpensive to deploy, and it can be widely applied in front crawl swimming lessons to help learners adjust their arm’s entry angle and body horizon to reduce forward drag and increase speed.</p> <p> </p>

Fuzzy and rough approach to the problem of missing data in fall detection system

Two new methods for mining incomplete data based on interval-valued fuzzy set theory and rough set theory, particularly maximal consistent blocks were proposed, and their application to the fall detection system, exactly to the posture detection system. The suggested methods are based on interval-valued aggregation operators supporting decision-making involving uncertainty. Additionally, the new measure of knowledge was used in two aspects: in the imputation of missing data and during the inference process. The usage of the knowledge measure allowed for a reduction of the uncertainty due to incompleteness and imprecision of data, while increasing the efficiency of the decision-making process, through the additional selection of objects for which the degree of the information measure is the highest. The developed hybrid approach to mining incomplete data and its application in a real decision-making problem yield stable performance despite the increase in missing data.

Multi-Mode Electric Wheelchair with Health Monitoring and Posture Detection Using Machine Learning Techniques

Patients with cognitive difficulties and impairments must be given innovative wheelchair systems to ease navigation and safety in today’s technologically evolving environment. This study presents a novel system developed to convert a manual wheelchair into an electric wheelchair. A portable kit has been designed so that it may install on any manual wheelchair with minor structural changes to convert it into an electric wheelchair. The multiple modes include the Joystick module, android app control, and voice control to provide multiple features to multiple disabled people. The proposed system includes a cloud-based data conversion model for health sensor data to display on an android application for easy access for the caretaker. A novel arrangement of sensors has been applied according to the accurate human body weight distribution in a sitting position that has greatly enhanced the accuracy of the applied model. Furthermore, seven different machine learning algorithms are applied to compare the accuracy, i.e., KNN, SVM, logistic regression, decision tree, random forest, XG Boost, and NN. The proposed system uses force-sensitive resistance (FSR) sensors with prescribed algorithms incorporated into wheelchair seats to detect users’ real-time sitting positions to avoid diseases, such as pressure ulcers and bed sores. Individuals who use wheelchairs are more likely to develop pressure ulcers if they remain in an inappropriate posture for an extended period because the blood supply to specific parts of their skin is cut off owing to increased pressure. Two FSR configurations are tested using seven algorithms of machine learning techniques to discover the optimal fit for a high-efficiency and high-accuracy posture detection system. Additionally, an obstacle detection facility enables one to drive safely in unknown and dynamic environments. An android application is also designed to provide users of wheelchairs with the ease of selecting the mode of operation of the wheelchair and displaying real-time posture and health status to the user or caretaker.

Research on the Efficiency of Working Status Based on Wearable Devices in Different Light Environments.

According to the working scenes, a proper light environment can enable people to maintain greater attention and meditation. A posture detection system in different working scenes is proposed in this paper, and different lighting conditions are provided for changes in body posture. This aims to stimulate the nervous system and improve work efficiency. A brainwave acquisition system was used to capture the participants’ optimal attention and meditation. The posture data are collected by ten miniature inertial measurement units (IMUs). The gradient descent method is used for information fusion and updating the participant’s attitude after sensor calibration. Compared with the optical capture system, the reliability of the system is verified, and the correlation coefficient of both joint angles is as high as 0.9983. A human rigid body model is designed for reconstructing the human posture. Five classical machine learning algorithms, including logistic regression, support vector machine (SVM), decision tree, random forest, and k-nearest neighbor (KNN), are used as classification algorithms to recognize different postures based on joint angles series. The results show that SVM and random forest achieve satisfactory classification effects. The effectiveness of the proposed method is demonstrated in the designed systematic experiment.

Poor posture detection system of hug using pose estimation

Poor posture detection system of hug using pose estimation

Smart walking assistant (SWA) for elderly care using an intelligent realtime hybrid model

This work implements a fall and posture detection system exploiting low cost sensors and applying machine learning to aid people in need both at indoor and outdoor. This intelligent system is able to identify fall with and without recovery within a stipulated period of time. In case of fall without recovery, an alert message along with date, time and location of fall is sent to relative/caregiver. This feature ensures real time assistance to avoid any criticality due to delay. In addition to this, an immediate last posture before the fall is also notified to identify the proneness of a person towards fall from a specific posture. This may aid clinical persons to take appropriate measures to prevent the future fall. The system is also able to take care of an unresponsive device after a fall (if any). We have designed and implemented this intelligent live fall with posture detection system, exploiting the sensors in micro processor unit (MPU) 6050 combined with low cost ESP 8266 micro-controller unit (MCU) using WiFi connectivity. The kinematic sensor data is collected at a rate of 40 Hz using accelerometer and gyroscope.The result shows that the system can identify the location and posture of the subject on regular interval along with the date and time of fall (if any). The emergency help system is aided with an audio-visual warning at the raspberry Pi based monitoring station along with a facility of sending the distress SMS.The system can operate either in manual or in auto mode. The dataset is prepared from local people of varied age groups (between 10 and 70 years) of both the genders.The system is tested randomly on 10 volunteers with an overall detection accuracy upto 98%.

LoRa-Based Smart IoT Application for Smart City: An Example of Human Posture Detection

Scientists have explored the human body for hundreds of years, and yet more relationships between the behaviors and health are still to be discovered. With the development of data mining, artificial intelligence technology, and human posture detection, it is much more possible to figure out how behaviors and movements influence people’s health and life and how to adjust the relationship between work and rest, which is needed urgently for modern people against this high-speed lifestyle. Using smart technology and daily behaviors to supervise or predict people’s health is a key part of a smart city. In a smart city, these applications involve large groups and high-frequency use, so the system must have low energy consumption, a portable system, and a low cost for long-term detection. To meet these requirements, this paper proposes a posture recognition method based on multisensor and using LoRa technology to build a long-term posture detection system. LoRa WAN technology has the advantages of low cost and long transmission distances. Combining the LoRa transmitting module and sensors, this paper designs wearable clothing to make people comfortable in any given posture. Aiming at LoRa’s low transmitting frequency and small size of data transmission, this paper proposes a multiprocessing method, including data denoising, data enlarging based on sliding windows, feature extraction, and feature selection using Random Forest, to make 4 values retain the most information about 125 data from 9 axes of sensors. The result shows an accuracy of 99.38% of extracted features and 95.06% of selected features with the training of 3239 groups of datasets. To verify the performance of the proposed algorithm, three testers created 500 groups of datasets and the results showed good performance. Hence, due to the energy sustainability of LoRa and the accuracy of recognition, this proposed posture recognition using multisensor and LoRa can work well when facing long-term detection and LoRa fits smart city well when facing long-distance transmission.

TORQUE ANALYSIS OF LOWER LIMB BASED ON UDWADIA–KALABA APPROACH AND INERTIAL SENSOR MOTION DETECTION

This paper presents an explicit dynamic equation of motion of human unilateral lower limb based on Udwadia–Kalaba approach and conducts experiments to verify the practicability of the novel approach. A three-segment, planar model with two-degrees-of-freedom is derived to represent the lower limb when the hip and knee do the extension-flexion motion as the human body lies flat. We reframe the joint torque analysis of lower limb as a problem of constrained motion in analytical dynamics, in which the joint torque supplied by a human body is treated as constraint torque and the motion as the constraint. A Wearable Human Posture Detection System (WHPDS) based on MEMS inertial sensors is designed. The original angle curves of hip and knee joints are explicitly obtained by WHPDS and used to calculate the joint constraint torques. No auxiliary variables such as Lagrangian multipliers or pseudo-generalized speeds are used in the dynamic modeling or analysis. Analysis of human unilateral lower limb can be helpful in many fields such as rehabilitation therapy. It is beneficial for assessing effectiveness in rehabilitation therapy and selecting appropriate rehabilitation strategies.

Smartphone-based swallowing monitoring and feedback device for mealtime assistance in nursing homes.

As many of the elderly are prone to dysphagia, preventing aspirations during mealtime assistance is a huge concern, especially for institutions operated with limited staff. Two of the main course of actions that are taken usually to prevent aspiration are the altering of bolus consistency and altering of the swallowing posture. To assist prevention of pneumonia, we have been developing a smartphone-based wearable technology that can evaluate swallowing ability in real-time. In this paper we will introduce modified version of Swallwoscope adding a posture detection system and a remote swallowing activity visualisation system to our previous work. We will evaluate the accuracy of the proposed posture detection system and also evaluate the feasibility of our device to determine bolus consistency. We plan to integrate these components in a network-based framework to share the relevant medical information within the community to enhance safety and pleasure of eating.

Balancing noise sensitivity, response latency, and posture accuracy for a computer-assisted canine posture training system

This paper describes a canine posture detection system composed of wearable sensors and instrumented devices that detect the postures sit, stand, and eat. The system consists of a customized harness outfitted with wearable Inertial Measurement Units (IMUs) and a base station for processing IMU data to classify canine postures. Research in operant conditioning, the science of behavior change, indicates that successful animal training requires consistent and accurate feedback on behavior. Properly designed computer systems excel at timeliness and accuracy, which are two characteristics most amateur trainers struggle with and professionals strive for. Therefore, in addition to the system being ergonomically designed to ensure the dog's comfort and well-being, it is engineered to provide posture detection with timing and accuracy on par with a professional trainer. We contend that providing a system with these characteristics will one day aid dogs in learning from humans by overcoming poor or ineffective timing during training. We present the initial steps in the development and validation of a computer-assisted training system designed to work outside of laboratory environments.The main contributions of this work are (a) to explore the trade-off between low-latency responses to changes in time-series IMU data representative of posture changes while maintaining accuracy and timing similar to a professional trainer, and (b) to provide a model for future ACI technologies by documenting the user-centered approach we followed to create a computer-assisted training system that met the criteria identified in (a). Accordingly, in addition to describing our system, we present the results of three experiments to characterize the performance of the system at capturing sit postures of dogs and providing timely reinforcement. These trade-offs are illustrated through the comparison of two algorithms. The first is Random Forest classification and the second is an algorithm which uses a Variance-based Threshold for classification of postures. Results indicate that with proper parameter tuning, our system can successfully capture and reinforce postures to provide computer-assisted training of dogs.

農作業知的支援のための PTZ カメラを用いた作業者位置姿勢検出システムの開発

農作業知的支援のための PTZ カメラを用いた作業者位置姿勢検出システムの開発