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Surface electromyography (sEMG) is an important measurement technique used in biomechanical, rehabilitation and sport environments. In this article the design, development and testing of a low-cost wearable sEMG system are described. The hardware architecture consists of a two-cascade small-sized bioamplifier with a total gain of 2,000 and band-pass of 3 to 500 Hz. The sampling frequency of the system is 1,000 Hz. Since real measured EMG signals are usually corrupted by various types of noises (motion artifacts, white noise and electromagnetic noise present at 50 Hz and higher harmonics), we have tested several denoising techniques, both on artificial and measured EMG signals. Results showed that a wavelet—based technique implementing Daubechies5 wavelet and soft sqtwolog thresholding is the most appropriate for EMG signals denoising. To test the system performance, EMG activities of six dominant muscles of ten healthy subjects during gait were measured (gluteus maximus, biceps femoris, sartorius, rectus femoris, tibialis anterior and medial gastrocnemius). The obtained EMG envelopes presented against the duration of gait cycle were compared favourably with the EMG data available in the literature, suggesting that the proposed system is suitable for a wide range of applications in biomechanics.

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The precise measurement and analysis of human movements is an essential step in biomechanical research used in sports or medicine. Measurement systems used for motion tracking should be non-invasive, safe to use, widely customisable and cost-efficient. In this study, complete design, development and evaluation of a high-speed optical motion tracking and analysis system is described. The system aims to analyse movements for sports and medical applications. The novelty of the proposed system is its design, which is based on visible light light-emitting diode (LED) markers, rather than infrared markers that are commonly used, and a pair of high-speed digital cameras. Calibration procedures and a super-resolution marker model are introduced, ensuring sub-pixel marker centre detection which results in higher three-dimensional reconstruction accuracy. Evaluation of the system included an accuracy test of the proposed system on static and moving objects with known dimensions, followed by analysis of kinematic data obtained in dynamic conditions while measuring human gait. The evaluation results are presented, and conclusions about system performance with possible improvements are discussed.

Gait patterns of humans and humanoid robots are often described by analysing changes in angular rotation of hip, knee and ankle joints during one gait cycle. Each joint displays specific behaviour and irregularities of the gait pattern could be detected by measuring displacements from the normal rotation curve, while small deviations of individual gait characteristics are usually not easily detected. In this paper, an advanced gait analysis method is proposed, which incorporates analysis of angular data and its derivations of hip, knee, and ankle joints, presented in the phase plane. The gait kinematics was measured using a system based on active markers and fast digital cameras. The experiment included measurements on thirty healthy, barefoot humans while walking on a treadmill. We also simulated types of irregular gait, by measurements on subjects wearing knee constraints. The new kinematic parameters which are introduced clearly indicated the discrepancy between normal, healthy gait trials and irregular gait trials. The proposed gait factor parameter is a valuable measure for the detection of irregularities in gait patterns of humans and humanoid robots

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The authors wish to make the following correction to this paper [1]. Due to an error Figure 15 was a duplicate of Figure 13, the former Figure 15 (labelled here as Previous Figure 15) should be replaced by the new version shown below (labeled here as New Figure 15):[...]