Ecological-Inspired System Design for Safety Manipulation Strategy in Home-care Robot

Yani, Mohamad; Besari, Adnan Rachmat Anom; Yamada, Nao; Kubota, Naoyuki

doi:10.1109/ccs49175.2020.9231354

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…The effectiveness of the proposed of affordance-effectivity integration has been proved in robot control [32], object manipulation tasks [33], imitation learning [34]. In our previous research, we have done the affordance-effectivity learning for robot climbing behaviors [35,36] and safe robot manipulations [37].…”

Section: Related Workmentioning

confidence: 99%

Topological mapping based on perceiving-acting cycle in sharing cognitive environments for robot partners

Ardilla

Yani

Saputra

et al. 2022

Communications in Information and Systems

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Various kinds of human-friendly robot partners have recently been developed to provide humans with superior services. Manipulation skills, including grasping, arranging, and delivering, are essential for home applications. A robot partner is designed to grasp the meaning of human behavior and their intention in shared spaces to assist older people at home. As a result, the robot partner requires the cognitive ability to comprehend states of the environment based on both people's and robot partners' physical and sensory embodiment. This research presents a human-robot interaction technique for handover behaviors based on cognitive contexts. First, we describe how to share a person's cognitive environment with a robot companion using the relevance idea presented in Cognitive Pragmatics. The perceived cognitive environment of humans contains a type of spatial topological structure, such as relative placement and proximity among objects. Furthermore, the human cognitive environment is continually updated due to the cyclic process of perception and action. As a result, we will look at how to apply topological mapping approaches in cognitive contexts. Next, using the idea of the perceiving-acting cycle presented in Ecological Psychology, we apply topological mapping methods of Growing Cell Structure (GCS) and Growing Neural Gas (GNG). The GCS represents the effectivity in the action system. In contrast, the GNG represents the human and robot task space. The experimental findings and real-world robot application examples indicate that the robot can correctly estimate human intention and conduct handover actions. Finally, we examine the effectiveness of the proposed approach and future research directions in the human-robot interaction based on the perceiving-acting cycle.

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Section: Related Workmentioning

confidence: 99%

Topological mapping based on perceiving-acting cycle in sharing cognitive environments for robot partners

Ardilla

Yani

Saputra

et al. 2022

Communications in Information and Systems

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“…The second issue was the lack of depth information in the collected data, represented in 2D pixel units. Since the egocentric approach requires three-dimensional understanding, we believe using an RGB-D camera can provide more consistent and detailed depth perception results [54]. Further research on incorporating sensory depth data could improve the effectiveness and applicability of this low-cost application.…”

Section: B Mesoscopic Model Evaluationmentioning

confidence: 99%

Multiscopic CPSS for Independent Block-Design Test Based on Hand–Object Interaction Recognition With Visual Attention

et al. 2023

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This paper introduces a multiscopic cyber-physical-social system (CPSS) to bridge the gap between independent rehabilitation in physical and cognitive aspects. Specifically, we focus on hand-object interaction (HOI) recognition with visual attention for the block-design test (BDT). The proposed framework utilizes three levels which consist of microscopic, mesoscopic, and macroscopic models. In the microscopic model, a hand-tracking vision captures hand-skeletal data and finger joint angle features, enabling the estimation of physical hand postures. In the mesoscopic model, an egocentric vision with an eye tracker records to hand and eye movements, allowing for the symbolic representation of hand-eye coordination through hand gestures and visual attention focus during the test. An evaluation vision system employs color feature classification in the macroscopic model to determine whether the design matches the given task. Through the first eight designs of WAIS-IV BDT with two scenarios, the system successfully measures human behavior from the physical to the cognitive domain. The experiment involving eight healthy participants investigates the relationship between physical measurement and cognitive evaluation. Regression and correlation analyses between the dominant and non-dominant hands reveal that evaluation indices (task completion time, skewness-kurtosis of hand posture, attention to pattern and blocks) can indicate improvement during BDT. The outcomes of this study have significant implications for clinicians and researchers, providing valuable information that is typically unavailable in clinical settings. The proposed multiscopic CPSS framework holds promise for advancing independent rehabilitation practices. Code and datasets are available online at https://github.com/anom-tmu/bdt-multiscopic.INDEX TERMS Kohs blocks design, self-rehabilitation, visual attention, hand-eye coordination.

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“…Furthermore, the Robot Operating System (ROS) is designed as a software system architecture to manage sensor inputs and outputs in many robotics applications. Many researchers are interested in developing a learning model for understanding the context of the environment from object detection and human behaviour estimations [1]- [4]. This capability affects the level of interaction with the environment and the action that is performed.…”

Section: Introductionmentioning

confidence: 99%

An efficient activity recognition for homecare robots from multi-modal communication dataset

Yani

Yamada

Siow

et al. 2023

Int. J. Adv. Intell. Informatics

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Human environments are designed and managed by humans for humans. Thus, adding robots to interact with humans and perform specific tasks appropriately is an essential topic in robotics research. In recent decades, object recognition, human skeletal, and face recognition frameworks have been implemented to support the tasks of robots. However, recognition of activities and interactions between humans and surrounding objects is an ongoing and more challenging problem. Therefore, this study proposed a graph neural network (GNN) approach to directly recognize human activity at home using vision and speech teaching data. Focus was given to the problem of classifying three activities, namely, eating, working, and reading, where these activities were conducted in the same environment. From the experiments, observations, and analyses, this proved to be quite a challenging problem to solve using only traditional convolutional neural networks (CNN) and video datasets. In the proposed method, an activity classification was learned from a 3D detected object corresponding to the human position. Next, human utterances were used to label the activity from the collected human and object 3D positions. The experiment, involving data collection and learning, was demonstrated by using human-robot communication. It was shown that the proposed method had the shortest training time of 100.346 seconds with 6000 positions from the dataset and was able to recognize the three activities more accurately than the deep layer aggregation (DLA) and X3D networks with video datasets.

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Ecological-Inspired System Design for Safety Manipulation Strategy in Home-care Robot

Cited by 6 publications

References 27 publications

Topological mapping based on perceiving-acting cycle in sharing cognitive environments for robot partners

Topological mapping based on perceiving-acting cycle in sharing cognitive environments for robot partners

Multiscopic CPSS for Independent Block-Design Test Based on Hand–Object Interaction Recognition With Visual Attention

An efficient activity recognition for homecare robots from multi-modal communication dataset

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