scholarly journals Evaluation of a Home Biomonitoring Autonomous Mobile Robot

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Enrique Dorronzoro Zubiete ◽  
Keigo Nakahata ◽  
Nevrez Imamoglu ◽  
Masashi Sekine ◽  
Guanghao Sun ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Success Rate ◽  
Activity Recognition ◽  
The Elderly ◽  
Daily Activities ◽  
Robot System ◽  
Observation Position ◽  
Series Of Experiments ◽  
Home Living ◽  
Watching Tv

Increasing population age demands more services in healthcare domain. It has been shown that mobile robots could be a potential solution to home biomonitoring for the elderly. Through our previous studies, a mobile robot system that is able to track a subject and identify his daily living activities has been developed. However, the system has not been tested in any home living scenarios. In this study we did a series of experiments to investigate the accuracy of activity recognition of the mobile robot in a home living scenario. The daily activities tested in the evaluation experiment include watching TV and sleeping. A dataset recorded by a distributed distance-measuring sensor network was used as a reference to the activity recognition results. It was shown that the accuracy is not consistent for all the activities; that is, mobile robot could achieve a high success rate in some activities but a poor success rate in others. It was found that the observation position of the mobile robot and subject surroundings have high impact on the accuracy of the activity recognition, due to the variability of the home living daily activities and their transitional process. The possibility of improvement of recognition accuracy has been shown too.

scholarly journals Development of fall detection and activity recognition using threshold based method and neural network

2020 ◽  
Vol 17 (3) ◽  
pp. 1338
Author(s):  
Sai Siong Jun ◽  
Hafiz Rashidi Ramli ◽  
Azura Che Soh ◽  
Noor Ain Kamsani ◽  
Raja Kamil Raja Ahmad ◽  
...  
Keyword(s):  
Neural Network ◽  
Activity Recognition ◽  
Human Activity ◽  
Daily Living ◽  
Fall Detection ◽  
The Elderly ◽  
Recognition System ◽  
Severe Injuries ◽  
Series Of Experiments

Falls are dangerous and contribute to over 80% of injury-related hospitalization especially amongst the elderly. Hence, fall detection is important for preventing severe injuries and accidental deaths. Meanwhile, recognizing human activity is important for monitoring health status and quality of life as it can be applied in geriatric care and healthcare in general. This research presents the development of a fall detection and human activity recognition system using Threshold Based Method (TBM) and Neural Network (NN). Intentional forward fall and six other activities of daily living (ADLs), which include running, jumping, walking, sitting, lying, and standing are performed by 15 healthy volunteers in a series of experiments. There are four important stages involved in fall detection and ADL recognition, which are signal filtering, segmentation, features extraction and classification. For classification, TBM achieved an accuracy of 98.41% and 95.40% for fall detection and activity recognition respectively whereas NN achieved an accuracy of 97.78% and 96.77% for fall detection and activity recognition respectively.

Comprehensive Ontological Model for Senior Wellness Activity Recognition in Smart Homes

2020 ◽  
pp. 148-167
Author(s):  
Hajar Khallouki ◽  
Rachid Benlamri ◽  
Abdulsalalm Yassine
Keyword(s):  
Activity Recognition ◽  
Smart Home ◽  
Environmental Parameters ◽  
Smart Homes ◽  
Well Being ◽  
The Elderly ◽  
Patient Profile ◽  
Ontological Model ◽  
Watching Tv ◽  
Lying Down

There are several works in the field of smart homes for healthcare, with different types of sensors used to monitor medical, behavioral and environmental parameters for patients. In the context of smart home for the elderly, the use of sensors needs to be adapted to respect the privacy of elders and to work passively without the need for caregiver assistance. Most research in this area focused on activity recognition (e.g. eating, sleeping, watching TV, etc.) which may be defined as the identification of a sequence of actions (e.g. using microwave, lying down, etc.). In this chapter, we propose a comprehensive ontological model for well-being activity recognition in smart home. Our approach takes into account different aspects of the well-being context such as patient profile, object being used to perform the activity, the time of running the activity, its location, etc. In order to validate the proposed ontology and reason on it, we perform a set of queries and inference rules.

scholarly journals Efficient Active Sensing with Categorized Further Explorations for a Home Behavior-Monitoring Robot

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Wenwei Yu ◽  
Keigo Nakahata ◽  
Guang Hao Sun ◽  
Akio Namiki ◽  
Sayuri Suwa ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Template Matching ◽  
Mobile Robotics ◽  
The Elderly ◽  
Target Object ◽  
Target Person ◽  
Active Sensing ◽  
Robot System ◽  
Body Contour ◽  
Behavior Monitoring

Mobile robotics is a potential solution to home behavior monitoring for the elderly. For a mobile robot in the real world, there are several types of uncertainties for its perceptions, such as the ambiguity between a target object and the surrounding objects and occlusions by furniture. The problem could be more serious for a home behavior-monitoring system, which aims to accurately recognize the activity of a target person, in spite of these uncertainties. It detects irregularities and categorizes situations requiring further explorations, which strategically maximize the information needed for activity recognition while minimizing the costs. Two schemes of active sensing, based on two irregularity detections, namely, heuristic-based and template-matching-based irregularity detections, were implemented and examined for body contour-based activity recognition. Their time cost and accuracy in activity recognition were evaluated through experiments in both a controlled scenario and a home living scenario. Experiment results showed that the categorized further explorations guided the robot system to sense the target person actively. As a result, with the proposed approach, the robot system has achieved higher accuracy of activity recognition.

Design of automatic recharging system of robot based on RFID radio frequency technology

2020 ◽  
Vol 13 ◽  
Author(s):  
Zheng Xiao
Keyword(s):  
Mobile Robot ◽  
Radio Frequency ◽  
Engineering Application ◽  
Data Communication ◽  
Computer Application ◽  
Wireless Data ◽  
Robot System ◽  
Communication Module ◽  
Upper Computer ◽  
Better Than

Background: In order to study the interference of wired transmission mode on robot motion, a mobile robot attitude calculation and debugging system based on radio frequency (RF) technology is proposed. Methods: Microcontroller STM32 has been used as the control core for the attitude information of the robot by using MEMS gyroscope and accelerometer. The optimal attitude Angle of the robot is calculated through nRF24L01 which is the core of the wireless communication module, attitude acquisition module and wireless data communication upper computer application platform. Results: The results shows that the positioning accuracy is better than±5mm. Conclusion: The experimental results show that the proposed attitude solving and debugging system of mobile robot based on RF technology has better reliability and real-time performance. The propped model is convenient for debugging of mobile robot system and has certain engineering application value.

scholarly journals Carebots

2016 ◽  
Vol 138 (09) ◽  
pp. S8-S13 ◽  
Author(s):  
Thiago Marinho ◽  
Christopher Widdowson ◽  
Amy Oetting ◽  
Arun Lakshmanan ◽  
Hang Cui ◽  
...  
Keyword(s):  
Older Adults ◽  
Multidisciplinary Approach ◽  
Human Perception ◽  
The Elderly ◽  
Comfort Level ◽  
Robot System ◽  
Perceived Safety ◽  
High Level ◽  
Future Work ◽  
Flying Robots

This article demonstrates a multidisciplinary approach that proposes to augment future caregiving by prolonged independence of older adults. The human–robot system allows the elderly to cooperate with small flying robots through an appropriate interface. ASPIRE provides a platform where high-level controllers can be designed to provide a layer of abstraction between the high-level task requests, the perceptual needs of the users, and the physical demands of the robotic platforms. With a robust framework that has the capability to account for human perception and comfort level, one can provide perceived safety for older adults, and further, add expressively that facilitates communication and interaction continuously throughout the stimulation. The proposed framework relies on an iterative process of low-level controllers design through experimental data collected from psychological trials. Future work includes the exploration of multiple carebots to cooperatively assist in caregiving tasks based on human-centered design approach.

A mobile robot system for automatic floor marking

2006 ◽  
Vol 23 (6-7) ◽  
pp. 441-459 ◽  
Author(s):  
Patric Jensfelt ◽  
Gunnar Gullstrand ◽  
Erik Förell
Keyword(s):  
Mobile Robot ◽  
Robot System

scholarly journals Metabolic Equivalents of Selected Sedentary and Physical Activities in Chinese Youth

2016 ◽  
Vol 13 (s1) ◽  
pp. S48-S52 ◽  
Author(s):  
Yong Gao ◽  
Haichun Sun ◽  
Jie Zhuang ◽  
Jian Zhang ◽  
Lynda Ransdell ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Computer Games ◽  
Resting Metabolic Rate ◽  
Physical Activities ◽  
Daily Activities ◽  
Chinese Youth ◽  
Metabolic Chamber ◽  
Metabolic Equivalents ◽  
Watching Tv ◽  
Jump Rope

Background:This study determined the metabolic equivalents (METs) of several activities typically performed by Chinese youth.Methods:Thirty youth (12 years) performed 7 activities that reflected their daily activities while Energy Expenditure (EE) was measured in a metabolic chamber.Results:METs were calculated as activity EE divided by participant’s measured resting metabolic rate. A MET value ranging from 0.8 to 1.2 was obtained for sleeping, watching TV, playing computer games, reading and doing homework. Performing radio gymnastics had a MET value of 2.9. Jumping rope at low effort required 3.1 METs. Except for watching TV, METs for other activities in this study were lower than Youth Compendium values.Conclusions:The results provide empirical evidence for more accurately assessing EE of activities commonly performed by Chinese youth. This is the first study to determine METs for radio gymnastics and jump rope in Chinese youth.

scholarly journals Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4132 ◽  
Author(s):  
Ku Ku Abd. Rahim ◽  
I. Elamvazuthi ◽  
Lila Izhar ◽  
Genci Capi
Keyword(s):  
Activity Recognition ◽  
Inertial Sensors ◽  
Wearable Sensors ◽  
Ensemble Methods ◽  
Daily Activities ◽  
Support Vector ◽  
Random Subspace ◽  
Ensemble Classifiers ◽  
Accuracy Rate

Increasing interest in analyzing human gait using various wearable sensors, which is known as Human Activity Recognition (HAR), can be found in recent research. Sensors such as accelerometers and gyroscopes are widely used in HAR. Recently, high interest has been shown in the use of wearable sensors in numerous applications such as rehabilitation, computer games, animation, filmmaking, and biomechanics. In this paper, classification of human daily activities using Ensemble Methods based on data acquired from smartphone inertial sensors involving about 30 subjects with six different activities is discussed. The six daily activities are walking, walking upstairs, walking downstairs, sitting, standing and lying. It involved three stages of activity recognition; namely, data signal processing (filtering and segmentation), feature extraction and classification. Five types of ensemble classifiers utilized are Bagging, Adaboost, Rotation forest, Ensembles of nested dichotomies (END) and Random subspace. These ensemble classifiers employed Support vector machine (SVM) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is evaluated with the holdout and 10-fold cross-validation evaluation methods. The performance of each human daily activity was measured in terms of precision, recall, F-measure, and receiver operating characteristic (ROC) curve. In addition, the performance is also measured based on the comparison of overall accuracy rate of classification between different ensemble classifiers and base learners. It was observed that overall, SVM produced better accuracy rate with 99.22% compared to RF with 97.91% based on a random subspace ensemble classifier.

scholarly journals Physical activity and stress coping in the elderly

2010 ◽  
Vol 8 (4) ◽  
pp. 419-422 ◽  
Author(s):  
Fernando de Andréa ◽  
Fernanda Varkala Lanuez ◽  
Adriana Nunes Machado ◽  
Wilson Jacob Filho
Keyword(s):  
Physical Activity ◽  
Exercise Program ◽  
The Elderly ◽  
Physical Activity Program ◽  
Daily Activities ◽  
Stress Coping ◽  
Coping Capacity ◽  
Positive Effects ◽  
Supervised Exercise ◽  
Activity Program

ABSTRACT Objective: To analyze the value of a physical activity program on stress coping of the elderly. Methods: Intervention study with a group of 18 elderly people referred by the Geriatric Service of the Hospital das Clinicas of the Universidade de Sao Paulo, who attended a supervised exercise program, evaluated by the human activity profile and the coping questionnaire. Results: In the coping and functional performance scales, increased stress coping capacity and improvement of daily activities were found after exposure to a physical activity program. Conclusions: The practice of supervised and regular physical activity, combining aerobic, resistance, stretching, and respiratory exercises, yields positive effects in the coping capacity and in the accomplishment of the daily activities.

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