Modelling and Experimental Evaluation of a Static Balancing Technique for a new Horizontally Mounted 3-UPU Parallel Mechanism

This paper presents the modelling and experimental evaluation of the gravity compensation of a horizontal 3-UPU parallel mechanism. The conventional Newton-Euler method for static analysis and balancing of mechanisms works for serial robots; however, it can become computationally expensive when applied to the analysis of parallel manipulators. To overcome this difficulty, in this paper we propose an approach, based on a Lagrangian method, that is more efficient in terms of computation time. The derivation of the gravity compensation model is based on the analytical computation of the total potential energy of the system at each position of the end-effector. In order to satisfy the gravity compensation condition, the total potential energy of the system should remain constant for all of the manipulator's configurations. Analytical and mechanical gravity compensation is taken into account, and the set of conditions and the system of springs are defined. Finally, employing a virtual reality environment, some experiments are carried out and the reliability and feasibility of the proposed model are evaluated in the presence and absence of the elastic components

Optimal feature set for smartphone-based activity recognition

Human activity recognition using wearable and mobile devices is used for decades to monitor humans’ daily behaviours. In recent years as smartphones being widely integrated into our daily lives, the use of smartphone’s built-in sensors in human activity recognition has been receiving more attention, in which smartphone accelerometer plays the main role. However, in comparison to the standard machine, when developing human activity recognition using a smartphone, the limitations such as processing capability and energy consumption should be taken into consideration, and therefore, a trade-off between performance and computational complexity should be considered. In this paper, we shed light on the importance of feature selection and its impact on simplifying the activity classification process, which enhances the computational complexity of the system. The novelty of this work is related to identifying the most efficient features for the detection of each individual activity uniquely. In an experimental study with human users and using different smartphones, we investigated how to achieve an optimal feature set, using which the system complexity can be decreased while the activity recognition accuracy remains high. For that, in the considered scenario, we instructed the participants to perform different activities, including static, dynamic, going up and down the stairs, and walking fast and slow while freely holding a smartphone in their hands. To evaluate the obtained optimal feature set implementing two major classification algorithms, the decision tree and the Bayesian network, we investigated activity recognition accuracy for different activities. We further evaluated the optimal feature set by comparing the performance of the activity recognition system using the optimal feature set and three feature sets taken from the state-of-the-art. The experimental results demonstrated that replacing a large number of conventional features with an optimal feature set has only a negligible impact on the overall activity recognition system performance while it can significantly decrease the system’s complexity, which is essential for smartphone-based systems

Feature extraction and feature selection in smartphone-based activity recognition

Nowadays, smartphones are gradually being integrated in our daily lives, and they can be considered powerful tools for monitoring human activities. However, due to the limitations of processing capability and energy consumption of smartphones compared to standard machines, a trade-off between performance and computational complexity must be considered when developing smartphone-based systems. In this paper, we shed light on the importance of feature selection and its impact on simplifying the activity classification process which enhances the computational complexity of the system. Through an in-depth survey on the features that are widely used in state-of-the-art studies, we selected the most common features for sensor-based activity classification, namely conventional features. Then, in an experimental study with 10 participants and using 2 different smartphones, we investigated how to reduce system complexity while maintaining classification performance by replacing the conventional feature set with an optimal set. For this reason, in the considered scenario, the users were instructed to perform different static and dynamic activities, while freely holding a smartphone in their hands. In our comparison to the state-of-the-art approaches, we implemented and evaluated major classification algorithms, including the decision tree and Bayesian network. We demonstrated that replacing the conventional feature set with an optimal set can significantly reduce the complexity of the activity recognition system with only a negligible impact on the overall system performance

Explainable robotics in human-robot interactions

This paper introduces a new research area called Explainable Robotics, which studies explainability in the context of human-robot interactions. The focus is on developing novel computational models, methods and algorithms for generating explanations that allow robots to operate at different levels of autonomy and communicate with humans in a trustworthy and human-friendly way. Individuals may need explanations during human-robot interactions for different reasons, which depend heavily on the context and human users involved. Therefore, the research challenge is identifying what needs to be explained at each level of autonomy and how these issues should be explained to different individuals. The paper presents the case for Explainable Robotics using a scenario involving the provision of medical health care to elderly patients with dementia with the help of technology. The paper highlights the main research challenges of Explainable Robotics. The first challenge is the need for new algorithms for generating explanations that use past experiences, analogies and real-time data to adapt to particular audiences and purposes. The second research challenge is developing novel computational models of situational and learned trust and new algorithms for the real-time sensing of trust. Finally, more research is needed to understand whether trust can be used as a control variable in Explainable Robotics

Explainability in human-robot teaming

In human-robot teaming, one of the crucial keys for the team’s success is that the robot and human teammates can collaborate accordingly in a coordinated manner. Each teammate should be aware of what the other teammate is going to perform and likely to need. In this context, a robot is expected to understand human teammate intention and performance and explain its actions and decisions and its rationale to its teammate. In addition, the capability to model the expectation of a human teammate empowers the robot to collaborate with human understandably and expectedly, leading to effective teaming. Through forming mental modelling, the robot can understand the impact of its own behaviour on the mental model of the human. In addition, the desirable traits in human-robot teaming, including fluent behaviour, adaptability, trust-building, effective communication, and explainability, can be achieved through mental modelling. In this work, we introduce a scenario for human-robot teaming considering all the five desirable traits in teaming with the main focus on explainability and effective communication. Using a general model reconciliation, the expectation of the human teammate of the robot can be modelled, and the explanation can be generated. In a considered scenario including Care-O-bot 4 service robot and a human teammate, we assume that the robot detects the human’s current task (analysing his body gesture) and predicts his following action and his expectation from the robot. In a reciprocal interdependence task, the robot coordinates his behaviour and acts accordingly by picking up the relevant tool. Through explanation and communication robot further offers the outcome of his decision to the human teammate and adapts its action by handing the tool to the human upon his desire

DFNB59 Gene Mutations and its Association with Deafness in Schoolchildren in Kohgilooyeh & Boyerahmad Province

Introduction & Objective: Hearing loss is a common disease affecting millions of people worldwide. Hearing loss can be caused due to genetic or environmental factors or even both. The genetic of hearing defect is highly heterogeneous and more than 100 genes are predicted to cause this disorder in humans. A newly identified gene (DFNB59) has been shown to cause deafness in some populations. Here we report mutation analysis for DFNB59 gene in 88 genetic non-syndromic hearing loss subjects. Materials & Methods: In this descriptive-lab based study which was conducted at the Cellular and Molecular Research Center of Shahrekord University of Medical Sciences, DNA was extracted from the peripheral blood samples using standard phenol chloroform procedure. Mutation analysis for DFNB59 gene was performed using PCR-SSCP/HA protocol. The suspected DFNB59 which was detected as shifted bands on PAGE were then confirmed by direct sequencing strategy. Results: Two DFNB59 polymorphisms including c.793C>G and c.793C>T were detected in 8 and 1 deaf subjects respectively. Conclusion: We conclude that there is no association between DFNB59 mutations and deafness in the studied patients in the region

DFNB59 Gene Mutation Screening Using PCR-SSCP/HA Technique in Non-syndromic Genetic Hearing Loss in Bushehr Province

Background: Hearing impairment (HI) is the most prevalent Neurosensory disorder which is heterogenous and can also occur due to environmental causes. The majority of hearing deficiencies are of genetic origin affecting about 60% of the HI cases. A novel gene DFNB59 encodes pejvakin has been recently shown to cause deafness. This study aims to determine the frequency of DFNB59 gene mutations in coding region the gene in Bushehr province. Methods: In this descriptive experimental study, we investigated the presence of DFNB59