The compliance and manipulability are the crucial properties for prosthetic hands design, especially considering both the usefulness and versatility. This study presents a novel mechatronic design of an underactuated prosthetic hand named HIT-VI hand. It consists of four modular underactuated fingers, one opposite thumb, and a set of multisensory embedded control system. Each modular finger uses one dc motor to drive its two active joints and one passive joint. Due to the use of an underactuated nine-bar mechanism, it can execute anthropomorphic coupled motion and self-adaptive motion. The hand contains 39 distributed force sensors, 10 angular position sensors, 6 current sensors, 1 temperature sensor, and 1 inertial measurement unit (IMU). The multisensory system enables the prosthetic hand to achieve intelligent position/force/current control. The fully integrated mechatronic design makes the prosthetic hand similar to an adult hand in size, weight, and appearance. Based on the kinematics and statics, the stability analysis of the prosthetic hand is carried out. Furthermore, the experiments executing the activities of daily living validate that the proposed hand has a respectable compliance and manipulability.

Elastic elements are commonly adopted to realize underactuation in the design of human-friendly prosthetic hands. The stiffness of these elastic elements, which is a key factor affecting the grasp performance of the underactuated finger, has not well addressed when considering both the stability and adaptability. In this study, an adaptive anthropomorphic finger that adopted a novel nine-bar mechanism is proposed. This nine-bar mechanism is integrated through a coupled four-bar mechanism and an adaptive seven-bar mechanism. The developed finger based on the nine-bar mechanism is able to improve the grasp stability in the global workspace under an extremely small spring stiffness. A quantitative analysis of the grasp stability was carried out. Comparative experiments on the grasps using the finger with/without adaptability were also performed. The results validated that our finger has a good stability when grasping the objects of different sizes.

For blind and visually impaired (BVI) amputees, the combined loss of vision and grasping abilities turns the seemingly simple task of reaching and grasping into a significant challenge. This paper introduces a novel multi-sensory prosthesis system designed for BVI amputees to assist in perception, navigation, and grasping tasks. The system integrates voice interaction, environmental perception, grasp guidance, collaborative control, and auditory/tactile feedback. Specifically, it processes user commands, provides environmental data via auditory/tactile channels, and manages collaborative control of grasp gestures and wrist angles for stable object handling. The prototype, viiathand, was experimentally tested with eight able-bodied and four blind subjects performing reach-and-grasp tasks, showing that users could accurately reach (average time:15.24s) and securely grasp objects (average time:17.23s) in an indoor setting. The system also proved to be user-friendly, requiring minimal training for users to become adept

In a medium-to-large earthquake, there are often reports of people being trapped or injured in elevators. This study investigates using an earthquake early warning (EEW) system, which provides seconds to tens of seconds warning before seismic waves arrive at a site, to help people escape from the elevators before a strong shaking arrives. However, such an application remains as a major engineering challenge due to the uncertainty of the EEW information and the short lead time available. A recent study presented an earthquake probability-based automated decision-making (ePAD) framework to address these issues. This paper focuses on studying the influence of two commonly ignored factors, uncertainty of warning and lead time, on the decision of stopping the elevators and opening the doors when an EEW message is received. Application of the ePAD framework requires using the performance-based earthquake engineering methodology for elevator damage prediction, making decision based on a cost-benefit mode...

For transradial amputees, especially those with insufficient residual muscle activity, it is challenging to quickly obtain an appropriate grasping pattern for a multigrasp prosthesis. To address this problem, this study proposed a fingertip proximity sensor and a grasping pattern prediction method base on it. Rather than exclusively utilizing the EMG of the subject for the grasping pattern recognition, the proposed method used fingertip proximity sensing to predict the appropriate grasping pattern automatically. We established a five-fingertip proximity training dataset for five common classes of grasping patterns (spherical grip, cylindrical grip, tripod pinch, lateral pinch, and hook). A neural network-based classifier was proposed and got a high accuracy (96%) within the training dataset. We assessed the combined EMG/proximity-based method (PS-EMG) on six able-bodied subjects and one transradial amputee subject while performing the “reach-and-pick up” tasks for novel objects. The assessments compared the performance of this method with the typical pure EMG methods. Results indicated that able-bodied subjects could reach the object and initiate prosthesis grasping with the desired grasping pattern on average within 1.93 s and complete the tasks 7.30% faster on average with the PS-EMG method, relative to the pattern recognition-based EMG method. And the amputee subject was, on average, 25.58% faster in completing tasks with the proposed PS-EMG method relative to the switch-based EMG method. The results showed that the proposed method allowed the user to obtain the desired grasping pattern quickly and reduced the requirement for EMG sources.