# Personalization of the 3D-Printed Upper Limb Exoskeleton Design--Mechanical and IT Aspects
### Possibilities  
Personalization improves usability and comfort of medical devices, including exoskeletons, and allows to solve a number of practical problems, e.g., observed differences in interaction between users and the system, user preferences, expected level and method of support. This applies to various activities (activities of daily living), from simple, related to self-service, to complex, e.g., climbing stairs while holding onto the handrail [7]. For the purposes of upper limb exoskeletons, it is already possible to automate or semi-automatically (based on AI) design 3D-printed chainmail with individually programmable properties--especially with variable stiffness/flexibility depending on the direction, with an adjustable one- or two-way bending module [8]. The model of human-machine cooperation is also subject to personalization. It is usually created using large datasets derived from kinematic analyzes of the users' elbow, wrist and finger movements. This is the basis for the development of a personalized exoskeleton prototype for the purpose of evaluating a specific modeling method and testing the prototype [9]. Innovative methods of design, adjustment and personalization are in great demand in this area, and not all of them are developed and widely available today. The inclusion of motor imaging (MI) signals from electroencephalography (EEG) in the exoskeleton control also requires an individual approach to design. This allows to better read the intention of the exoskeleton user's movement [10]. This is a great step towards bionic neuroprostheses, which are the future of the development of this group of solutions (including the feeling of thermal sensations and the sense of shape or surface even from amputated organs). This would allow people with dentures to function better. The development of personalized exoskeletons will be necessary due to the constant pursuit of physical improvement and overcoming physical limitations, including in people with congenital or acquired deficits [11]. The Assist as Needed (AAN) algorithm is already used to control the elbow exoskeleton. Force Sensing Sensor (FSR) and electromyography sensors on the biceps provide data for machine learning algorithms personalized for each patient and havereached an accuracy of up to 91.22%. The challenge is to provide patients with real-time visual feedback on their movement and for learning how to control the exoskeleton using VR [12]. From gait studies, kinematic data is known to provide general characteristics of movement efficiency, but limited conclusions can be drawn about neural control strategies, and some kinematic/kinetic parameters are a consequence of exoskeleton control. Aforementioned data should be supplemented with an assessment of muscular coordination and control strategies [13]. Surface electromyography (sEMG) and machine learning-based adaptive controllers of upper limb exoskeletons require personalization. They have achieved an average accuracy of over 93% for users with muscular dystrophy and neurodegenerative diseases without previous exposure to these types of controllers [14]. Upper limb exoskeletons support individually selected neuromotor rehabilitation including restoring the function of the upper limb and improving the quality of life of patients. The effectiveness of rehabilitation depends on the possibility of introducing personalized training plans and the availability of objective assessment of the patient's progress (monitoring the effects of the above-mentioned rehabilitation plans) [15]. As part of the personalization, a device was also developed to dynamically adjust the load on the exoskeleton (torque adjustment) taking into account its posture [16]. Another element of personalization is patient-oriented biofeedback based on human-exoskeleton interaction. This improves the compliance of the patient with the exoskeleton in terms of joint movement strategies and user participation strategies by means of audio and vibration cues [17].