Rapid and Flexible 3D Printed Finger Prostheses With Soft Fingertips: Technique and Clinical Application

We present a method for fabricating passive finger prostheses with soft fingertips by utilizing 3D scanning and 3D printing with flexible filament. The proposed method uses multi-process printing at varying infill levels to provide soft fingertips to emulate biological fingers. The proposed method also enables rapid prototyping of finger prostheses, and the flexibility to change interphalangeal joint angles to fit the prostheses for different manipulation and occupational therapy tasks. The entire process of designing and fabricating the prostheses can be conducted in one day. The presented technique uses scan data of the intact side fingers to provide the shape and contour of the finger prostheses, while the socket is designed based on the scan data of the amputation side. The paper presents the developed technique and its clinical application. Experiments are conducted to measure the stiffness of the printed material at varying infill levels and the stiffness of the printed fingertips. The results are compared to measurements of biological fingertip stiffness from the literature. The clinical application includes two cases, one case with distal phalanx loss on the thumb, index, and middle fingers, and one case with distal and middle phalanx loss on the middle and ring fingers. Fitting was successful for both recipients and they were both able to use the prostheses successfully.

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The Internet of Federated Things (IoFT)

The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the “cloud” will be substituted by the “crowd” where model training is brought to the edge, allowing IoT devices to collaboratively extract knowledge and build smart analytics/models while keeping their personal data stored locally. This paradigm shift was set into motion by the tremendous increase in computational power on IoT devices and the recent advances in decentralized and privacy-preserving model training, coined as federated learning (FL). This article provides a vision for IoFT and a systematic overview of current efforts towards realizing this vision. Specifically, we first introduce the defining characteristics of IoFT and discuss FL data-driven approaches, opportunities, and challenges that allow decentralized inference within three dimensions: (i) a global model that maximizes utility across all IoT devices, (ii) a personalized model that borrows strengths across all devices yet retains its own model, (iii) a meta-learning model that quickly adapts to new devices or learning tasks. We end by describing the vision and challenges of IoFT in reshaping different industries through the lens of domain experts. Those industries include manufacturing, transportation, energy, healthcare, quality & reliability, business, and computing.

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