Wrist and finger motion recognition via M-mode ultrasound signal: A feasibility study

Parametric Production of Prostheses Using the Additive Polymer Manufacturing Technology Multi Jet Fusion

This study aims to develop a procedure for the production of 3D-printed forearm prostheses (especially hard outer sockets). The production procedure is designed in the form of a parametric workflow (CAD model), which significantly speeds up the designing process of the prosthesis. This procedure is not fixedly dependent on the software (SW) equipment and is fully transferable into another SW environment. The use of these prostheses will significantly increase the comfort of their patients' lives. It is possible to produce prostheses faster and in larger amounts and variants by the usage of additive technology. The input for the own production of the prosthesis is a model of the internal soft socket of the patient. This soft socket (soft bed) is made by a qualified prosthetist. A 3D-scanned CAD model is obtained afterward using the scanning method by an automatic laser projector. An editable, parametric external socket (modifiable in any CAD format) is generated from the obtained 3D scan using a special algorithmic model. This socket, after the necessary individual modifications, is transferred to 3D printing technology and produced using powder technology Multi Jet Fusion (HP MJF). The result of the designed and tested procedure is a quickly editable 3D-printed outer socket (main part of prosthesis), which is able to fully replace the current long-fiber composite solution. Production of current solutions is relatively time-consuming, and only one piece is produced in a given time. The newly designed technology eliminates this. This study summarized the possibilities of speeding up the production of forearm prostheses (but not only these) by creating a parametric CAD model that is applicable to different patients.

Controlling Upper Limb Prostheses Using Sonomyography (SMG): A Review

This paper presents a critical review and comparison of the results of recently published studies in the fields of human-machine interface and the use of sonomyography (SMG) for the control of upper limb prothesis. For this review paper, a combination of the keywords "Human Machine Interface", "Sonomyography", "Ultrasound", "Upper Limb Prosthesis", "Artificial Intelligence", and "Non-Invasive Sensors" was used to search for articles on Google Scholar and PubMed. Sixty-one articles were found, of which fifty-nine were used in this review. For a comparison of the different ultrasound modes, feature extraction methods, and machine learning algorithms, 16 articles were used. Various modes of ultrasound devices for prosthetic control, various machine learning algorithms for classifying different hand gestures, and various feature extraction methods for increasing the accuracy of artificial intelligence used in their controlling systems are reviewed in this article. The results of the review article show that ultrasound sensing has the potential to be used as a viable human-machine interface in order to control bionic hands with multiple degrees of freedom. Moreover, different hand gestures can be classified by different machine learning algorithms trained with extracted features from collected data with an accuracy of around 95%.

Multi-Sensing Techniques with Ultrasound for Musculoskeletal Assessment: A Review

The study of muscle contractions generated by the muscle-tendon unit (MTU) plays a critical role in medical diagnoses, monitoring, rehabilitation, and functional assessments, including the potential for movement prediction modeling used for prosthetic control. Over the last decade, the use of combined traditional techniques to quantify information about the muscle condition that is correlated to neuromuscular electrical activation and the generation of muscle force and vibration has grown. The purpose of this review is to guide the reader to relevant works in different applications of ultrasound imaging in combination with other techniques for the characterization of biological signals. Several research groups have been using multi-sensing systems to carry out specific studies in the health area. We can divide these studies into two categories: human-machine interface (HMI), in which sensors are used to capture critical information to control computerized prostheses and/or robotic actuators, and physiological study, where sensors are used to investigate a hypothesis and/or a clinical diagnosis. In addition, the relevance, challenges, and expectations for future work are discussed.