Introduction: Sockets and interfaces are important elements for lower limb orthoses and prostheses, as they allow for proper contact and fit between the devices and the affected limb or stump.Objective: To review the different polymers used in the development of lower limb sockets and external prosthetic and orthotic interfaces, their functional requirements and the possible skin problems caused by their use.Materials and methods: A literature review was conducted using the databases EBSCO, Embase, LILACS, SciELO, ScienceDirect and Scopus.Results: 47 articles and papers that met the inclusion criteria were retrieved. Thermoplastics, thermosets, foams, gels and elastomers are among the polymers used for manufacturing prosthetic and orthotic interfaces and sockets. However, studies estimate that between 32% and 90.9% of the population that use these devices have experienced skin problems on the affected stump or limb, such as excessive sweating, wounds and irritation.Conclusion: There is a clear need for further research to develop prosthetic and orthotic interfaces and sockets for lower limbs that can prevent or control damage to the skin of users.

Landmine victims require an engaging and immersive rehabilitation process to maintain motivation and therapeutic adherence, such as virtual reality games. This paper proposes a virtual reality exercise game called Exogames, which works with Nukawa, a lower limb rehabilitation robot (LLRR). Together, they constitute the general Kina system. The design and development process of Exogames is reported, as well as the evaluation of its potential for physical and emotional rehabilitation. In an initial survey designed ad-hoc, 13 health professionals evaluated compliance with various requirements. They agreed that Exogames would help the user focus on rehabilitation by providing motivation; 92.3% said that the user will feel safe in the virtual world, 66.7% of them agreed or totally agreed that it presents characteristics that may enhance the physical rehabilitation of lower limbs for amputees, 83.3% stated that it would promote the welfare of landmine victims, and 76.9% responded that the graphical interface and data report are useful for real-time assessment, and would be helpful for four interventional areas in all rehabilitation stages. In a second evaluation, using standardized surveys, five physical therapists and one lower limb amputee tried the Kina system as users. They filled out the System Usability Scale (SUS), the Physical Activity Enjoyment Scale (PACES), and the Game Experience Questionnaire (GEQ). The usability of the Kina system overall score was 69 (66, 79) out of 100, suggesting an acceptable though improvable usability. The overall PACES score of 110 (108, 112) out of 126 suggests that users enjoyed the game well. Finally, users indicated a positive effect with a good sense of immersion and smooth of gameplay during the tests, as indicated by the GEQ results. In summary, the evaluations showed that Exogames has the potential to be used as a virtual reality game for the physical and emotional rehabilitation of landmine victims.

In this paper, we report the integration of a Kinect sensor device with a robotic arm Kuka KR6 as a master-slave communication for applications of teleoperation and bioengineering. The Kinect device captures the position of the user's right hand, filters its resulting data and detects its movement using Haar wavelets. This data is then sent to the Kuka KR6 controller and it moves using forward kinematics. A series of tests were implemented to evaluate the accuracy of the slave system position compared to the user's right hand position using the Pearson correlation coefficient method. It resulted in a very strong correlation between them.

Lower limb rehabilitation robot (LLRR) users, to successfully conduct isotonic exercises, require real-time feedback on the torque they exert on the robot to meet the goal of the treatment. Still, direct torque measuring is expensive, and indirect encoder-based estimation strategies, such as inverse dynamics (ID) and Nonlinear Disturbance Observers (NDO), are sensitive to Body Segment Inertial Parameters (BSIPs) uncertainties. We envision a way to minimize such parametric uncertainties. This paper proposes two human–robot interaction torque estimation methods: the Identified ID-based method (IID) and the Identified NDO-based method (INDO). Evaluating in simulation the proposal to apply, in each rehabilitation session, a sequential two-phase method: (1) An initial calibration phase will use an online parameter estimation to reduce sensitivity to BSIPs uncertainties. (2) The torque estimation phase uses the estimated parameters to obtain a better result. We conducted simulations under signal-to-noise ratio (SNR) = 40 $\mathrm{d}$$\mathrm{B}$ and 20% BSIPs uncertainties. In addition, we compared the effectiveness with two of the best methods reported in the literature via simulation. Both proposed methods obtained the best Coefficient of Correlation, Mean Absolute Error, and Root Mean Squared Error compared to the benchmarks. Moreover, the IID and INDO fulfilled more than 72.2% and 88.9% of the requirements, respectively. In contrast, both methods reported in the literature only accomplish 27.8% and 33.3% of the requirements when using simulations under noise and BSIPs uncertainties. Therefore, this paper extends two methods reported in the literature and copes with BSIPs uncertainties without using additional sensors.

A direct blow to the knee is one way to injure the anterior cruciate ligament (ACL), e.g., during a football or traffic accident. Robot-assisted therapy (RAT) rehabilitation, simulating regular walking, improves walking and balance abilities, and extensor strength after ACL reconstruction. However, there is a need to perform RAT during other phases of ACL injury rehabilitation before attempting an advanced exercise such as walking. This paper aims to propose a myoelectric control (MEC) algorithm for a robot-assisted rehabilitation system, “Nukawa”, to assist knee movement during these types of exercises, i.e., such as in active-assisted extension exercises. Surface electromyography (sEMG) signal processing algorithm was developed to detect the motion intention of the knee joint. The sEMG signal processing algorithm and the movement control algorithm, reported by the authors in a previous publication, were joined together as a hardware-in-the-loop simulation to create and test the MEC algorithm, instead of using the actual robot. An experimental protocol was conducted with 17 healthy subjects to acquire sEMG signals and their lower limb kinematics during 12 ACL rehabilitation exercises. The proposed motion intention algorithm detected the orientation of the intention 100% of the times for the extension and flexion exercises. Also, it detected in 94% and 59% of the cases the intensity of the movement intention in a comparable way to the maximum voluntary contraction (MVC) during extension exercises and flexion exercises, respectively. The maximum position mean absolute error was $$0.1^{\circ }$$ 0 . 1 ∘ , $$6.3^{\circ }$$ 6 . 3 ∘ , and $$0.3^{\circ }$$ 0 . 3 ∘ for the hip, knee, and ankle joints, respectively. The MEC algorithm detected the intensity of the movement intention, approximately, in a comparable way to the MVC and the orientation. Moreover, it requires no prior training or additional torque sensors. Also, it controls the speed of the knee joint of Nukawa to assist the knee movement, i.e., such as in active-assisted extension exercises.

Arthrogenic muscle inhibition (AMI) refers to muscular alterations that are generated, producing biomechanical motor control and movement problems, leading to deficiencies in strength and atrophy. Currently, there exist methods that involve virtual reality (VR) and have been well perceived by physiotherapists. The present research measured the potential benefits in terms of therapeutic adherence and speed of recovery, through a comparative analysis in a healthcare provider institution, in Medellín, Colombia, with and without the aid of VR. For this purpose, dynamometry, and surface electromyography (sEMG) signal acquisition tools were used. The treatment involved neuromodulation, ranges of motion and mobility work, strengthening and reintegration into movement, complemented with TENS, NMENS and therapeutic exercise, where the patient was expected to receive a satisfactory and faster adherence and recovery. A group of 15 people with AMI who include at least 15 min of VR per session in their treatment were compared with another group who received only the base treatment, i.e., the control group. Analyzing the variables individually, it is possible to affirm that VR, as a complement, statistically significantly improved the therapeutic adherence in 33.3% for CG and 37.5% for IG. Additionally, it increased strength with both legs, the symmetry between them, and decreased the level of pain and stiffness that is related to mobility.