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This paper aims to describe the design of a multi-degree of freedom (DOF) prosthetic hand prototype implementing postural synergy mechanically, which is actuated by two motors via a transmission unit, and is controlled using surface electromyography (sEMG) signal.

First, an anthropomorphic robotic hand is designed to imitate the human hand. The robotic hand has 18 DOF, 12 of which are actively driven by Bowden cables. Next, a set of different grasp modes are performed on a “full actuation” robotic hand, and principal component analysis (PCA) method is used to extract the first two postural synergies. Then, they are used to design a differential pulley-based transmission unit using two independent inputs to drive 12 output tendons. Finally, two control signals extracted from six channels of sEMG signals are used to proportionally control the two motors for achieving hand posture synthesis.

Using a differential pulley-based mechanical transmission unit to implement the synthesis of the first two postural synergies can make the prosthetic hand achieve different grasps by two motors, such as power, precision and lateral grasps. It is also feasible to control this “two actuation” prosthetic hand by relating the two-dimensional sEMG inputs with the first two postural synergies.

Mechanical implantation of postural synergies reduces the number of independent actuators without sacrificing the prosthetic hand’s versatility and simplifies its controller. Two-dimensional control extracted from sEMG is mapped into the combination coefficients of postural synergy synthesis. It shows potential application in the practical prosthetic hand.

This paper aims to propose an innovative kinematic control algorithm for redundant robotic manipulators. The algorithm takes advantage of a bio-inspired approach.

A simplified two-degree-of-freedom model is presented to handle kinematic redundancy in the x-y plane; an extension to three-dimensional tracking tasks is presented as well. A set of sample trajectories was used to evaluate the performances of the proposed algorithm.

The results from the simulations confirm the continuity and accuracy of generated joint profiles for given end-effector trajectories as well as algorithm robustness, singularity and self-collision avoidance.

This paper shows how to control a redundant robotic arm by applying human upper arm-inspired concept of inter-joint dependency.

This paper aims to propose a novel method based on a gesture primitives analysis of human daily grasping tasks for designing dexterous hands with various grasping and in-hand manipulation abilities, which simplifies the complex and redundant humanoid five-finger hand system.

First, the authors developed the fingers and the joint configuration with a series of gesture primitives configurations and the modular virtual finger scheme, refined from the daily work gesture library by principal component analysis. Then, the authors optimized the joint degree-of-freedom configuration with the bionic design analysis of the anatomy, and the authors optimized the dexterity workspace. Furthermore, the adaptive fingertip and routing structure were designed based on the dexterous manipulation theory. Finally, the effectiveness of the design method was experimentally validated.

A novel lightweight three-finger and nine-degree-of-freedom dexterous hand with force/position perception was designed. The proposed routing structure was shown to have the capability of mapping the relationship between the joint space and actuator space. The adaptive fingertip with an embedded force sensor can effectively increase the robustness of the grasping operation. Moreover, the dexterous hand can grasp various objects in different configurations and perform in-hand manipulation dexterously.

The dexterous hand design developed in this study is less complex and performs better in dexterous manipulation than previous designs.

In this paper, we propose a general cybernetical approach for simulating the erect stance posture control under perturbation (postural adjustment and postural reaction). For that, a reasonably complicated biomechanical model of a human body is proposed with a general motor control architecture. This cybernetical modelling method solves the body dynamic equilibrium and the trajectories tracking problems using a strategy based on the “Trunk center of mass acceleration control” and on a force distribution on each leg and each arm. The motor control scheme is composed of two levels: the “coordinator” level devoted to control legs and arms movements (trajectories tracking) and a global equilibrium of the body and “limbs” level which ensures the dynamic control of the limbs. Simulation results have been proposed to validate this cybernetical method.

The purpose of this paper is to propose a gait analysis technique that aims to identify differences and similarities in gait performance between three different assistive devices (ADs).

Two feature reduction techniques, linear principal component analysis (PCA) and nonlinear kernel-PCA (KPCA), are expanded to provide a comparison of the spatio-temporal, symmetrical indexes and postural control parameters among the three different ADs. Then, a multiclass support vector machine (MSVM) with different approaches is designed to evaluate the potential of PCA and KPCA to extract relevant gait features that can differentiate between ADs.

Results demonstrated that symmetrical indexes and postural control parameters are better suited to provide useful information about the different gait patterns that total knee arthroplasty (TKA) patients present when walking with different ADs. The combination of KPCA and MSVM with discriminant functions (MSVM DF) resulted in a noticeably improved performance. Such combination demonstrated that, with symmetric indexes and postural control parameters, it is possible to extract with high-accuracy nonlinear gait features for automatic classification of gait patterns with ADs.

The information obtained with the proposed technique could be used to identify benefits and limitations of ADs on the rehabilitation process and to evaluate the benefit of their use in TKA patients.

The physically-demanding and repetitive nature of construction work often exposes workers to work-related musculoskeletal injuries. Real-time information about the ergonomic consequences of workers' postures can enhance their ability to control or self-manage their exposures. This study proposes a digital twin framework to improve self-management ergonomic exposures through bi-directional mapping between workers' postures and their corresponding virtual replica.

The viability of the proposed approach was demonstrated by implementing the digital twin framework on a simulated floor-framing task. The proposed framework uses wearable sensors to track the kinematics of workers' body segments and communicates the ergonomic risks via an augmented virtual replica within the worker's field of view. Sequence-to-sequence long short-term memory (LSTM) network is employed to adapt the virtual feedback to workers' performance.

Results show promise for reducing ergonomic risks of the construction workforce through improved awareness. The experimental study demonstrates feasibility of the proposed approach for reducing overexertion of the trunk. Performance of the LSTM network improved when trained with augmented data but at a high computational cost.

Suggested actionable feedback is currently based on actual work postures. The study is experimental and will need to be scaled up prior to field deployment.

This study reveals the potentials of digital twins for personalized posture training and sets precedence for further investigations into opportunities offered by digital twins for improving health and wellbeing of the construction workforce.

This paper aims to optimize the stiffness coefficient of the elastic element for a passive waist assistive exoskeleton (WAE). There is a tradeoff between stiffness coefficient of elastic element of the exoskeleton and work efficiency of the wearer, because elastic element affects original bending motion of the wearer and the force requirement of erector spinae is compensated by the other muscles. However, there is no accepted conclusion on how to determine the proper stiffness coefficient, especially with respected to the effort of groups of muscles, not only erector spinae.

In this study, a consumption indicator based on muscle fatigue of seven muscles is proposed and a Bayesian-based human-in-the-loop optimization approach is adopted to optimize the stiffness coefficient. Pneumatic artificial muscles are used to replace the mechanical elastic part to adjust the assistive force automatically. The proposed optimization method is verified by the way of load-lifting experiments with three different conditions: without exoskeleton, with fixed air pressure and with optimized air pressure. Six subjects participated in the experiment and each experiment is performed in different day.

Compared with No-Exo condition and static assistance condition, the parameter-optimized waist exoskeleton averagely reduces muscle fatigue of the six subjects by 45.30 ± 29.14% and 30.94 ± 30.29%, respectively. The experimental results indicate that the proposed method is effective to reduce muscle fatigue during stoop lifting task.

This paper provides a novel cost function construction method based on muscle fatigue and muscle synergy for passive WAE stiffness optimization.

This chapter discussed the implementation of the digital twin (DT) idea into construction. Through the adoption of DTs into construction practices, construction professionals have been able to project an identical virtual concept of sections of the project execution right from the onset. In the introduction and discussing of its origin, the DT was further assessed about its applications in construction beneficial in enhancing project delivery. Other sections like barriers, drivers and benefits of the DT in construction summarised what this chapter represents in terms of discussing the new involvement of digital tools in construction execution, management and sustainability.

This paper aims to develop an adaptable control architecture for electrohydraulic humanoid robots (HYDROïD) that emulate the functionality of the human nervous system. The developed control architecture overcomes the limitations of classical centralized and decentralized systems by distributing intelligence across controllers.

The proposed solution is a distributed real-time control architecture with robot operating system (ROS). The joint controllers have the intelligence to make decisions, dominate their actuators and publish their state. The real-time capabilities are ensured in the master controller by using a Preempt-RT kernel beside open robot control software middleware to operate the real-time tasks and in the customized joint controllers by free real-time operating systems firmware. Systems can be either centralized, where all components are connected to a central unit or decentralized, where distributed units act as interfaces between the I/Os and the master controller when the master controller is without the ability to make decisions.

The proposed architecture establishes a versatile and adaptive control framework. It features a centralized hardware topology with a master PC and distributed joint controllers, while the software architecture adapts based on the task. It operates in a distributed manner for precise, force-independent motions and in a decentralized manner for tasks requiring compliance and force control. This design enables the examination of the sensorimotor loop at both low-level joint controllers and the high-level master controller.

It developed a control architecture emulating the functionality of the human nervous system. The experimental validations were performed on the HYDROïD. The results demonstrated 50% advancements in the update rate compared to other humanoids and 30% in the latency of the master processor and the control tasks.

Dewey argues throughout Democracy and Education that schooling plays a powerful role in forming how we are disposed towards democracy. Disposition underlies and determines both thinking and activity. A disposition which operates habitually tends to maintain the moral, social and intellectual status quo. A humane democracy demands a disposition which both challenges existing conditions and is concerned to change them for social well-being. A student’s experience at school would ideally need to be one which supports the motivation and skills to foster such a democracy. Dewey claims that we dispose ourselves to think in particular ways. If our mental processes are habitual then teaching and pastoral care may be done in a way that might impose rigidity of thought on students. If the intelligent concern for social well-being is missing from our thinking, the educational experience we offer provides neither model nor means for the development of a humane democracy. Using vignettes from our own experience as educators, together with our interpretation of Dewey’s thinking in Democracy and Education and How We Think, we consider how our own mental processes as educators and dispositions which underlie them might impact for good or for ill on students’ day-to-day experience. We argue that the main responsibility for conditions of experience falls on policymakers, school leadership, management and teachers, who, we conclude with Dewey, should aim to be aware of their disposition and its manifestation in thinking and activity in order to create conditions which make schooling a truly democratic experience.