# Overview of 3D Printed Exoskeleton Materials and Opportunities for Their AI-Based Optimization
## 3 Development of Technologies and Applications  
3D printing in combination with AI increases versatility and allows for the process or product to be adapted to specific needs, often resulting in faster development/modification of the project, greater efficiency, and greater savings [16]. Even cheap exoskeletons can already generate traffic efficiently using open-source BCI configurations (e.g., OpenViBE) with an accuracy of 86% [17]. Combined metal-polymer elements can be a technological breakthrough in the field of 3D-printed exoskeletons. It is all about the strength properties of the combination of the metal shell and the metal-polymer filler [18]. The design challenges include, above all, the development of a 3D-printed exoskeleton of the hand with five fingers, physiological ranges of motion, adequate strength, and possible grips [19]. Workis still underway to standardize and maintain the quality of 3D-printed medical devices, which will grow as the technology matures [20]. In the process of designing and testing a 3D-printed exoskeleton, it is necessary to ensure the greatest usability and comfort, mainly through personalization, fitting, and testing [21]. The main component used to propel exoskeletons are artificial muscles, although current methods of producing them are difficult and laborious and require thorough computational simulations [22]. An effective and practical design of a 3D printed chainmail with programmed directional functions for the exoskeleton of the hand with variable stiffness/flexibility, depending on the direction, was also developed [23]. Often, also for exoskeletons, a rigid acrylonitrile butadiene styrene (ABS) structure is combined in the support area with a flexible thermoplastic polyurethane (TPU) structure [24]. Flexo soft exoskeleton weighs 330 g (with battery) and provides grip on objects up to 81 mm in diameter and grip force up to 48 N and grip on objects up to 81 mm in diameter when controlled via wireless sEMG or smartphone app for rehabilitation programs via Bluetooth [25]. Traditional rigid exoskeletons have numerous disadvantages, such as limited mobility, safety, ergonomics, autonomy, and high costs. Hence the attempts to develop a soft, textile exoskeleton (using 3D printed components and ready-made fabrics) to support elbow flexion and extension as well as opening and closing the hand with low power consumption, low weight, and low cost. The movement is controlled by a machine learning algorithm based on a system model built based on sensory data [26]. Similarly, a 3D-printed exoskeleton is pneumatically actuated by soft modules (EAsoftM) with passive joints (compensating for gravity) and active joints (moving the shoulder and elbow joints) [27]. The 3D printing of exoskeletons for children with motor deficits enables rapid prototyping, low costs, and easy adjustment to the patient's dimensions [28]. Exoskeletons for wrist rehabilitation still have disadvantages, such as heavy weight, an uncertain trajectory of movement, etc. However, stable training of wrist rehabilitation in three degrees of freedom with compact and lightweight device characteristics can be achieved [29]. Exoskeletons can also be used by patients who have lost hand function due to burns [30]. A new, cheap, and effective in-home rehabilitation 3D printed exoskeleton for hand rehabilitation may have a spring and cable drive to transmit movement and force. In healthy individuals, activation of the extensor muscles of the fingers and flexors of the wrist increased by 184.1% and 197.8%, respectively. The weight of the device, less than 400 g, is still large [31]. Spinal cord injuries (SCI) also affect hand function and general movement. The 3D-printed hand-held exoskeleton (made of PLA) allows for control using electromyographic (EMG) signals and supports grip functions in patients after SCI in the cervical section (with tetraplegia) [32]. The Soft Flexible 3D Printed Composite Actuator (SECA) offers flexion and extension controlled by the predictive control method of the iterative learning model (ILMPC).