Model inference takes only 64 s and consumes 442 J energy. MARS is evaluated on the Nvidia Jetson Xavier-NX board. To the best of our knowledge, this is the first rehabilitation movements dataset using mmWave point cloud. We evaluate MARS using ten specific rehabilitation movements performed by four human subjects involving all body parts and obtain an average mean absolute error of 5.87 cm for all joint positions. MARS can reconstruct 19 human joints and their skeleton from the point cloud generated by mmWave radar. Then, it uses a convolution neural network (CNN) to estimate the accurate location of human joints. It first maps the 5D time-series point cloud from mmWave to a lower dimension. MARS provides a low-cost solution with a competitive object localization and detection accuracy.
We propose a millimeter-wave (mmWave)-based assistive rehabilitation system (MARS) for motor disorders to address these challenges. However, they have high-cost, raise serious privacy concerns, and require strict lighting and placement settings. Camera-based systems have been popular for capturing joint motion. Human joint estimation is a substantial component of these programs since it offers valuable visualization and feedback based on body movements. New approaches are needed to allow patients to perform prescribed exercises at their homes and alleviate commuting requirements, expert shortages, and healthcare costs. The current practice is performing rehabilitation exercises under clinical expert supervision. Rehabilitation is a crucial process for patients suffering from motor disorders. In this case, all the other methods of superresolution with respect to the maximum likelihood method are known to be quasi-optimal. The range estimation method proposed in this paper and based on the maximum likelihood can potentially guarantee the attainment of the lower Cramer-Rao bound for dispersions of unbiased estimates of signal parameters. Based on the theory of multichannel analysis, the paper presents a system of equations derived that ensures the system solution in the case of several targets. In addition, the nonorthogonal frequency plan prevents the communications surveillance means to determine the distance to radar using the same phase difference of subcarriers that is possible, in principle, while using the fixed frequency grid in the case of OFDM signals. Such an approach makes it possible to implement an adaptive offset from frequency concentrated interference by the selection of values of subcarrier frequencies that are least susceptible to negative impact, to reduce the peak factor of a multifrequency signal mixture, and improve the electromagnetic compatibility of radar equipment at the expense of signal frequency bandwidth narrowing. It allows us to take into account the Doppler shift of subcarriers, provide for the control of range resolution magnitude and the attainable signal-to-noise ratio. An advantage of the proposed method is the possibility of arbitrary variations of multifrequency signal parameters, including frequencies of harmonic components (subcarriers) and the length of sample observed. The non-orthogonal signal based methods considered in this paper could be viewed as a more general case with respect to OFDM. The fundamental distinction of the approach proposed in this paper is the use of multifrequency nonorthogonal signals (N-OFDM) in which the location of frequencies of harmonic components is not tied to maximums of the amplitude-frequency characteristics (AFC) of filters synthesized by using the fast Fourier transform (FFT).
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All this in full measure can be referred to the well-known methods of superresolution. In addition, such methods feature limited spectral efficiency and noise immunity. However, the measurement methods based on using these signals lead to errors in range finding in the presence of Doppler frequency shift.
At present, the OFDM type orthogonal signals have gained widespread use for solving the range finding problems. The phase multifrequency methods of radar range measurements from the viewpoint of solving the problem of digital spectral analysis are proposed in this study.
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