This work will elaborate the fundamental principles of physical artificial intelligence (Physical AI) from a scientific and systemic perspective. The aim is to create a theoretical foundation that describes the physical embodiment, sensory perception, ability to act, learning processes, and context sensitivity of intelligent systems within a coherent framework. While classical AI approaches rely on symbolic processing and data driven models, Physical AI understands intelligence as an emergent phenomenon of real interaction between body, environment, and experience. The six fundamentals presented here are embodiment, sensory perception, motor action, learning, autonomy, and context sensitivity, and form the conceptual basis for designing and evaluating physically intelligent systems. Theoretically, it is shown that these six principles do not represent loose functional modules but rather act as a closed control loop in which energy, information, control, and context are in constant interaction. This circular interaction enables a system to generate meaning not from databases, but from physical experience, a paradigm shift that understands intelligence as an physical embodied process. Physical AI understands learning not as parameter adjustment, but as a change in the structural coupling between agents and the environment. To illustrate this, the theoretical model is explained using a practical scenario: An adaptive assistant robot supports patients in a rehabilitation clinic. This example illustrates that physical intelligence does not arise from abstract calculation, but from immediate, embodied experience. It shows how the six fundamentals interact in a real system: embodiment as a prerequisite, perception as input, movement as expression, learning as adaptation, autonomy as regulation, and context as orientation.

Joowan Kim and Jaeheung Park are the corresponding authors. Abstract This paper presents a novel rehabilitation robot designed to address the challenges of passive range of motion (PROM) exercises for frozen shoulder patients by integrating advanced scapulohumeral rhythm stabilization. Frozen shoulder is characterized by limited glenohumeral motion and disrupted scapulohumeral rhythm, with therapist-assisted interventions being highly effective for restoring normal shoulder function. While existing robotic solutions replicate natural shoulder biomechanics, they lack the ability to stabilize compensatory movements, such as shoulder shrugging, which are critical for effective rehabilitation. Our proposed device features a 6 degrees of freedom (DoF) mechanism, including 5 DoF for shoulder motion and an innovative 1 DoF Joint press for scapular stabilization. The robot employs a personalized two-phase operation: recording normal shoulder movement patterns from the unaffected side and applying them to guide the affected side. Experimental results demonstrated the robot's ability to replicate recorded motion patterns with high precision, with root mean square error (RMSE) values consistently below 1 degree. These findings confirm the robot's potential as a rehabilitation tool capable of automating PROM exercises while correcting compensatory movements. The system provides a foundation for advanced, personalized rehabilitation for patients with frozen shoulders. Keywords: Rehabilitation robot, Shoulder exercise, Scapulohumeral rhythm, Compensatory movements 1 Introduction Frozen shoulder, also known as adhesive capsulitis, is a debilitating condition characterized by pain and progressive loss of shoulder movement [1]. The underlying cause of a frozen shoulder is not fully understood, and it is associated with inflammation and thickening of the capsule that surrounds the shoulder joint where the glenoid of the scapula and the proximal humerus meet [1, 2]. The condition affects approximately 2-5% of the general population, with a higher incidence in people aged in the mid-50s [3]. Frozen shoulder problems extend beyond limited range of motion (ROM), involving compensatory movements that can lead to secondary issues. These compensations mainly include excessive scapular upward rotation and trunk adjustments during arm elevation [4]. Especially, excessive scapular upward rotation results as shoulder shrugging, which directly affects the scapulohumeral rhythm [5, 6]. Scapulohumeral rhythm, traditionally described as a 2:1 ratio between glenohumeral elevation and scapular upward rotation, but in reality it is a nonlinear motion, is crucial for shoulder stability [7-9].

Rehabilitation robots are often used in game-like interactions for rehabilitation to increase a person's motivation to complete rehabilitation exercises. By adjusting exercise difficulty for a specific user throughout the exercise interaction, robots can maximize both the user's rehabilitation outcomes and the their motivation throughout the exercise. Previous approaches have assumed exercises have generic difficulty values that apply to all users equally, however, we identified that stroke survivors have varied and unique perceptions of exercise difficulty. For example, some stroke survivors found reaching vertically more difficult than reaching farther but lower while others found reaching farther more challenging than reaching vertically. In this paper, we formulate a causal tree-based method to calculate exercise difficulty based on the user's performance. We find that this approach accurately models exercise difficulty and provides a readily interpretable model of why that exercise is difficult for both users and caretakers.

In this paper, we address the development of a robotic rehabilitation system for the upper limbs based on collaborative end-effector solutions. The use of commercial collaborative robots offers significant advantages for this task, as they are optimized from an engineering perspective and ensure safe physical interaction with humans. However, they also come with noticeable drawbacks, such as the limited range of sizes available on the market and the standard control modes, which are primarily oriented towards industrial or service applications. To address these limitations, we propose an optimization-based design method to fully exploit the capability of the cobot in performing rehabilitation tasks. Additionally, we introduce a novel control architecture based on an admittance-type Virtual Fixture method, which constrains the motion of the robot along a prescribed path. This approach allows for an intuitive definition of the task to be performed via Programming by Demonstration and enables the system to operate both passively and actively. In passive mode, the system supports the patient during task execution with additional force, while in active mode, it opposes the motion with a braking force. Experimental results demonstrate the effectiveness of the proposed method.

In this pioneering study, we unveiled a groundbreaking approach for actuating rehabilitation robots through the innovative use of magnetic technology as a seamless haptic force generator, offering a leap forward in enhancing user interface and experience, particularly in end-effector-based robots for upper-limb extremity motor rehabilitation. We employed the Extended Kalman Filter to meticulously analyze and formalize the robotic system's nonlinear dynamics, showcasing the potential of this sophisticated algorithm in accurately tracking and compensating for disturbances, thereby ensuring seamless and effective motor training. The proposed planar robotic system embedded with magnetic technology was evaluated with the recruitment of human subjects. We reached a minimum RMS value of 0.2 and a maximum of 2.06 in our estimations, indicating our algorithm's capability for tracking the system behavior. Overall, the results showed significant improvement in smoothness, comfort, and safety during execution and motor training. The proposed novel magnetic actuation and advanced algorithmic control opens new horizons for the development of more efficient and user-friendly rehabilitation technologies.

In recent years, gamification has become very popular for rehabilitating different cognitive and motor problems. It has been shown that rehabilitation is effective when it starts early enough and it is intensive and repetitive. However, the success of rehabilitation depends also on the motivation and perseverance of patients during treatment. Adding serious games to the rehabilitation procedure will help the patients to overcome the monotonicity of the treatment procedure. On the other hand, if a variety of games can be used with a robotic rehabilitation system, it will help to define tasks with different levels of difficulty with greater variety. In this paper we introduce a procedure for connecting a rehabilitation robot to several web-based games. In other words, an interface is designed that connects the robot to a computer through a USB port. To validate the usefulness of the proposed approach, a researcher designed survey was used to get feedback from several users. The results demonstrate that having several games besides rehabilitation makes the procedure of rehabilitation entertaining.

Robotic devices for clinical rehabilitation of patients with neurological impairments come in a wide variety of shapes and sizes and employ different kinds of actuators. The design process for rehabilitation robots is driven by the intention that the technical system will be paired with a human being; it is of paramount importance that safety and flexibility of operation are ensured. When designing a robotic device for people with paretic limbs it is usually desirable to specify the actuators and controllers in such a way that a degree of compliance and yielding is retained, rather than forcing the limbs to rigidly follow a pre-programmed trajectory. This reduces the likelihood of injury which might result from forcing a stiff joint to move in a non-physiological manner, and it allows the patient to positively interact with the system and actively guide the therapy. It is not uncommon to come across the viewpoint that electric actuators are not well suited to applications having compliant design requirements: in traditional control engineering, DC motors are programmed to provide accurate and fast setpoint tracking; it is often thought that they are not ideally suited for clinical rehabilitation tasks where "soft" behavioural characteristics are called for.

According to a recent report by Credence Research, the global medical robotics market was valued at $7.24 billion in 2015 and is expected to grow to $20 billion by 2023. A key driver for this growth is demand for using robots in minimally invasive surgeries, especially for neurologic, orthopedic, and laparoscopic procedures. As a result, a wide range of robots is being developed to serve in a variety of roles within the medical environment. Robots specializing in human treatment include surgical robots and rehabilitation robots. The field of assistive and therapeutic robotic devices is also expanding rapidly.

This article provides an overview of the state of the art in this area. It begins with the dominant paradigm of assistive control, from impedance-based cooperative controller through electromyography and intention estimation. It then covers challenge-based algorithms, which provide more difficult and complex tasks for the patient to perform through resistive control and error augmentation. Furthermore, it describes exercise adaptation algorithms that change the overall exercise intensity based on the patient's performance or physiological responses, as well as socially assistive robots that provide only verbal and visual guidance. The article concludes with a discussion of the current challenges in rehabilitation robot software: evaluating existing control strategies in a clinical setting as well as increasing the robot's autonomy using entirely new artificial intelligence techniques.

OSAKA – Manufacturers are stepping up the development of health care rehabilitation robots because the global market for such equipment is expected to grow rapidly. Companies of all kinds are trying to tap into a medical robot market that is forecast to grow 24.51 percent through 2020, according to market researcher Infiniti Research. Demand for rehabilitation robots could be particularly strong in Japan, given the rapidly graying and shrinking population. The robots are expected to help elderly people regain the use of limbs and avoid becoming bedridden. They also allow patients to train without assistance, a major attraction in a nation with a dwindling working-age population and a mushrooming number of retirees.