The unique musculoskeletal structure of the human hand brings in wider dexterous capabilities to grasp and manipulate a repertoire of objects than the non-human primates. It has been widely accepted that the orientation and the position of the thumb plays an important role in this characteristic behavior. There have been numerous attempts to develop anthropomorphic robotic hands with varying levels of success. Nevertheless, manipulation ability in those hands is to be ameliorated even though they can grasp objects successfully. An appropriate model of the thumb is important to manipulate the objects against the fingers and to maintain the stability. Modeling these complex interactions about the mechanical axes of the joints and how to incorporate these joints in robotic thumbs is a challenging task. This article presents a review of the biomechanics of the human thumb and the robotic thumb designs to identify opportunities for future anthropomorphic robotic hands.

Abstract. There have been numerous attempts to develop anthropomorphic robotic hands with varying levels of dexterous capabilities. However, these robotic hands often suffer from a lack of comprehensive understanding of the musculoskeletal behavior of the human thumb with integrated foldable palm. This paper proposes a novel kinematic model to analyze the importance of thumb-palm embodiment in grasping objects. The model is validated using human demonstrations for five precision grasp types across five human subjects. The model is used to find whether there are any co-activations among the thumb joint angles and muskuloskeletal parameters of the palm. In this paper we show that there are certain pairs of joints that show stronger linear relationships in the torque space than in joint angle space. These observations provide useful design guidelines to reduce control complexity in anthropomorphic robotic thumbs.

Early detection of fire is key to mitigate fire related damages. This paper presents a differential pyro-electric infrared (PIR) sensor and deep neural networks (DNNs) based method to detect fire in real-time. Since the PIR sensor is sensitive to sudden body motions and emits a continuous time-varying signal, experiments are carried out to collect human and fire motions using a PIR sensor. These signals are processed using one-dimensional continuous wavelet transform to perform feature extraction. The corresponding wavelet coefficients are converted into RGB spectrum images that are then used as inputs for a deep convolutional neural network. Various pre-trained DNN architectures are adopted to train and identify the collected data for background (no motion), human motion, and fire categories: small quasi-static and spreading fires. Experimental results show that the ShuffleNet architecture yields the highest prediction accuracy of 87.8%. Experimental results for the real-time strategy which works at a speed of 12 frames-per-second show 95.34% and 92.39% fire and human motion detection accuracy levels respectively.

People who use the thumb in repetitive manipulation tasks are likely to develop thumb related impairments from excessive loading at the base joints of the thumb. Biologically informed wearable robotic assistive mechanisms can provide viable solutions to prevent occurring such injuries. This paper tests the hypothesis that an external assistive force at the metacarpophalangeal joint will be most effective when applied perpendicular to the palm folding axis in terms of maximizing the contribution at the thumb-tip as well as minimizing the projections on the vulnerable base joints of the thumb. Experiments conducted using human subjects validated the predictions made by a simplified kinematic model of the thumb that includes a foldable palm, showing that: 1) the palm folding angle varies from 71.5 • to 75.3 • (from the radial axis in the coronal plane) for the four thumb-finger pairs and 2) the most effective assistive force direction (from the ulnar axis in the coronal plane) at the MCP joint is in the range 0 • < ψ < 30 • for the four thumb-finger pairs. These findings provide design guidelines for hand assistive mechanisms to maximize the efficacy of thumb external assistance.