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Motion intention detection is fundamental in the implementation of human-machine interfaces applied to assistive robots. In this paper, multiple machine learning techniques have been explored for creating upper limb motion prediction models, which generally depend on three factors: the signals collected from the user (such as kinematic or physiological), the extracted features and the selected algorithm. We explore the use of different features extracted from various signals when used to train multiple algorithms for the prediction of elbow flexion angle trajectories. The accuracy of the prediction was evaluated based on the mean velocity and peak amplitude of the trajectory, which are sufficient to fully define it. Results show that prediction accuracy when using solely physiological signals is low, however, when kinematic signals are included, it is largely improved. This suggests kinematic signals provide a reliable source of information for predicting elbow trajectories. Different models were trained using 10 algorithms. Regularization algorithms performed well in all conditions, whereas neural networks performed better when the most important features are selected. The extensive analysis provided in this study can be consulted to aid in the development of accurate upper limb motion intention detection models. Agency for Science, Technology and Research (A*STAR) Published version This work was partially supported by the grant “Intelligent Human-Robot interface for upper limb wearable robots” (Award Number SERC1922500046, A*STAR, Singapore).

In this paper, we present the modelling of a hyper-redundant mechanism for assisting fingers in achieving desired configurations. The device is composed of serially linked rotating rigid elements and is actuated by a cable-driven mechanism that can provide both flexion and extension. The modelling is based on the analysis of the static equilibrium configurations that the structure acquires, which are dependent on the design parameters of each segment and on the ratio of the tensions applied on the cables. We show how it is possible to obtain different configurations by properly shaping the geometry of each element. Such a relationship was theoretically investigated and experimentally validated to compare the actual configuration with the one predicted by the model. The prototype was designed so that its static equilibrium configuration approximates the geometry of a finger during flexion-extension. It was observed that the customised structure achieves a configuration more closely resembling the desired one than a non-customised actuator. By adapting the design of each element and properly adjusting the ratio of tensions, one is able to compute the final configuration achieved by the actuator so that it can approximate the desired posture.

Humans are favoured to conventional robotics for some tasks in industry due to their increased dexterity and fine motor skills, however, performance of these tasks can result in injury to the user at a cost to both the user and the employer. In this paper we describe a lightweight, upper-limb exosuit intended to assist the user during lifting tasks (up to 10kg) and while operating power tools, which are common activities for industrial workers. The exosuit assists elbow and shoulder flexion for both arms and allows for passive movements in the transverse plane. To achieve the design criteria an underactuated mechanism has been developed, where a single motor is used to assist two degrees of freedom per arm. In the intended application, the hands are generally busy and cannot be used to provide inputs to the robot, therefore, a voice-activated control has been developed that allows the user to give voice commands to operate the exosuit. Experiments were performed on 5 healthy subjects to assess the change in Muscular Activation (MA), inferred through Electromyography (EMG) signals, during three tasks: i) lifting and releasing a load; ii) holding a position and iii) manipulating a tool. The results showed that the exosuit is capable of reducing EMG activity (between 24.6% and 64.6%) and the recognition rate (94.8%) of the voice recognition module was evaluated. This work was partially supported by the "Smart Exosuits for Human Augmentation in Industrial Working Environment" project funded by Parts Precision Technology (S) Pte Ltd (project definition: M4062147).