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Our research is focused on the development of an at-home health care biomonitoring mobile robot for the people in demand. Main task of the robot is to detect and track a designated subject while recognizing his/her activity for analysis and to provide warning in an emergency. In order to push forward the system towards its real application, in this...
Citations
... Our studies showed that mobile robotics is a potential solution to home behavior monitoring for the elderly [9][10][11]. A replacement of smart house with a mobile robot reduces the number of sensors which in turn reduces the cost of implementation and the deployment complexity. ...
... Moreover and importantly, it provides seamless temporal and spatial monitoring for safe home daily living if the robot is well controlled. It was reported in [9][10][11] that a mobile robot was capable of tracking a target subject and performing tasks such as observations and analyses of the environment and recognition and analyses of the behaviors of the subject, by using the location information and the extracted body contour features of the subject. The system showed an excellent performance: 98.6-99.4% as an overall correct rate of human activity recognition in testing datasets. ...
... It is for detecting and tracking novelties using the environment map of the robot as a top-down approach without the necessity of large amount of training data. Using geometric features calculated from human body contour extracted from depth images, the system can identify six different activities: standing, walking, bending, sitting, lying down, and falling [9][10][11]. ...
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.
... The autonomous robot ( Figure 1) uses Pioneer P3-DX (Adept MobileRobots) as its platform. It includes a Lidar (Light Detection and Ranging) and a Kinect (Microsoft) sensor on a rotation table [14]. ...
... The overall correct rate of our human activity recognition of those experiments was 98.6-99.4% [15]. The activity recognition could be further improved by making full use of localization information to deal with partial occlusion [14]. However, in those experiments, the activities were performed in a static and repeated manner; that is, after one activity was carried out repeatedly, at one certain place, another activity was tested. ...
... Moreover, the control parameters of the system have been empirically explored under several environment changes and subject variation, to establish the optimal control strategy to perform the subject tracking and activity recognition [14]. ...
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.
With many applications across domains as diverse as logistics, healthcare, and agriculture, service robots are in increasingly high demand. Nevertheless, the designers of these robots often struggle with specifying their tasks in a way that is both human-understandable and sufficiently precise to enable automated verification and planning of robotic missions. Recent research has addressed this problem for the functional aspects of robotic missions through the use of
mission specification patterns
. These patterns support the definition of robotic missions involving, for instance, the patrolling of a perimeter, the avoidance of unsafe locations within an area, or reacting to specific events. Our paper introduces a catalog of
QUantitAtive RoboTic mission spEcificaTion patterns
(QUARTET) that tackles the complementary and equally important challenge of specifying the reliability, performance, resource usage, and other key quantitative properties of robotic missions. Identified using a methodology that included the analysis of 73 research papers published in 17 leading software engineering and robotics venues between 2014–2021, our 22 QUARTET patterns are defined in a tool-supported domain-specific language. As such, QUARTET enables: (i) the precise definition of quantitative robotic-mission requirements and (ii) the translation of these requirements into probabilistic reward computation tree logic (PRCTL), supporting their formal verification and automated planning of robotic missions. We demonstrate the applicability of QUARTET by showing that it supports the specification of over 95% of the quantitative robotic mission requirements from a systematically selected set of recent research papers, of which 75% can be automatically translated into PRCTL for the purposes of verification through model checking and mission planning.
It has been shown that mobile robots could be a potential solution to home bio-monitoring for the elderly. Through our previous studies, a mobile robot system that is able to recognize daily living activities of a target person has been developed. However, in a home environment, there are several factors of uncertainty, such as confusion with surrounding objects, occlusion by furniture, etc. Thus, the features extracted could not guarantee the correct recognition. To solve the problem, we applied active sensing strategy to the robot, especially to the body contour based behavior recognition part, by implementing 3 algorithms in a row, which enabled (1) judging irregularity of feature extraction; (2) adjusting robot viewpoints accordingly; (3) avoiding excessive viewpoint adjustment based on a short-term memory mechanism, respectively. As a result of experiment in a home living scenario, higher activity recognition accuracy was achieved by the proposed active sensing algorithms.
