The purpose of this study was to investigate the validity of automatically stored exercise data from the elastic band sensor compared with those of a gold-standard stretch sensor during exercises commonly used for rehabilitation of the hip and knee. The design was a concurrent validity study. Participants performed 3 sets of 10 repetitions of 6 exercises with both sensors attached to the same elastic exercise band. These were knee extension, knee flexion, hip abduction and adduction, hip flexion, and hip external rotation. Agreement between methods was calculated for date, time of day, repetitions, total and single repetition, and contraction phase-specific time under tension (TUT). Files from the elastic band sensor contained identical dates, time of day, and number of repetitions for each exercise set compared with those for the gold standard. Total TUT and total single repetition TUT were highly correlated with the stretch sensor (r = 0.83-0.96) but lower for contraction phase-specific TUTs (r = 0.45-0.94). There were systematic differences between the methods ranging from 0.0 to 2.2 seconds (0.0-6.3%) for total TUT and total single repetition TUT, and between 0.0 and 3.3 seconds (0.0-33.3%) for contraction phase-specific TUTs. The elastic band sensor is a valid measure of date, time of day, number of repetitions and sets, total TUT, and total single repetition TUT during commonly used home-based strength training exercises. However, the elastic band sensor seems unable to validly measure TUT for specific contraction phases.

The system was feasible for adolescents with patellofemoral pain. The system made it possible to capture detailed data about the TUT, repetitions and sets during home-based exercises together with pain intensity before and after each exercise. [Rathleff MS, Bandholm T, McGirr KA, Harring SI, Sørensen AS, Thorborg K (2016) New exercise-integrated technology can monitor the dosage and quality of exercise performed against an elastic resistance band by adolescents with patellofemoral pain: an observational study.Journal of Physiotherapy62: 159-163].

Image processing involving correlation based filter algorithms have proved extremely useful for image enhancement, feature extraction and recognition, in a wide range of medical applications, but is almost exclusively used with still images due to the amount of computations required by the correlations. In this paper, we present two different practical methods for applying correlation-based algorithms to real-time video images, using hardware accelerated correlation, as well as our results in applying the method to optical venography. The first method employs a GPU accelerated personal computer, while the second method employs an embedded FPGA. We will discuss major difference between the two approaches, and their suitability for clinical use. The system presented detects blood vessels in human forearms in images from NIR camera setup for the use in a clinical environment.