The present study aimed to validate the perceived exertion scale based on the repetitions in reserve (RIR) of Paralympic Powerlifting (PP) athletes. Twenty-one PP athletes were assessed in the one-maximum repetition test (1RM) of the bench press exercise, maximum repetition strength tests with loads corresponding to 90, 85, 80, and 75% of 1RM, and 4-repetitions strength tests (4-repST) with 100, 90, 85, 80, and 75% of the 1RM. The RIR scale was assessed after each set of the 4-repST and compared to the 1RM and maximum strength tests. For criterion validity, the total estimated repetition was significantly lower compared to the maximum strength test repetitions in the lower scores of the RIR scale (median=7.0 vs. 9.0 for 75% of 1RM). The total estimated repetition correlation with maximum strength was very high and significant (ICC=0.91). Using the Bland and Altman method, the difference between means was 0.9 reps, and the interval around differences was 6.4 reps. For construct validity, and the RIR scale presented high correlation with 1RM intensities (rho=0.86, p≤0.05). The RIR scale was validated and coaches and sports trainers can use this monitoring tool to ensure submaximal loads when improving lift technique, which is extremely rigorous in PP.

The use of assistive technologies can mitigate or reduce the challenges faced by individuals with motor disabilities to use computer systems. However, those who feature severe involuntary movements often have fewer options at hand. This work describes an application that can recognize the user’s head using a conventional webcam, track its motion, model the desired functional movement, and recognize it to enable the use of a virtual keyboard. The proposed classifier features a flexible structure and may be personalized for different user need. Experimental results obtained with participants with no neurological disorders have shown that classifiers based on Hidden Markov Models provided similar or better performance than a classifier based on position threshold. However, motion segmentation and interpretation modules were sensitive to involuntary movements featured by participants with cerebral palsy that took part in the study.

Objectives: To establish predictive equations for peak torque of muscle groups with totally and partially preserved innervation in individuals with motor complete spinal cord injury (SCI), based on hand dynamometry and strength predictor variables. Methods: The cross-sectional study conducted at a rehabilitation hospital consecutively recruited 108 men and women with SCI. All participants performed maximum peak torque tests for shoulder abduction/adduction (isokinetic), trunk flexion/extension (isometric), and handgrip strength testing (hand dynamometer) to establish predictive peak torque equations. The primary outcomes were peak torque variables. Handgrip strength, age, injury level, time since injury, age at injury, body mass, height, body mass index, and physical activity level were the secondary outcomes used as strength predictor variables. Results: Handgrip strength was a predictor variable for shoulder abduction/adduction peak torque. The best predictive models for shoulder abduction/adduction peak torque exhibited R ² = 0.57 and R ² = 0.60, respectively ( p ≤ .05). Injury level showed the highest significant predictive capacity for trunk flexion/extension peak torque models ( R ² = 0.38 and R ² = 0.29; p ≤ .05). Conclusion: Shoulder abduction/adduction peak torque predictive equations may be an alternative for use in an accessible strength tool (hand dynamometry) to evaluate training and rehabilitation programs. Trunk flexion/extension peak torque equations exhibited moderate correlations and high standard error of the estimates and should be used with caution.