Statistical Analysis by Semiparametric Additive Regression and LSTM-FCN Based Hierarchical Classification for Computer Vision Quantification of Parkinsonian Bradykinesia

Bradykinesia, characterized by involuntary slowing or decrement of movement, is a fundamental symptom of Parkinson's Disease (PD) and is vital for its clinical diagnosis. Despite various methodologies explored to quantify bradykinesia, computer vision-based approaches have shown promising results. However, these methods often fall short in adequately addressing key bradykinesia characteristics in repetitive limb movements: "occasional arrest" and "decrement in amplitude." This research advances vision-based quantification of bradykinesia by introducing nuanced numerical analysis to capture decrement in amplitudes and employing a simple deep learning technique, LSTM-FCN, for precise classification of occasional arrests. Our approach structures the classification process hierarchically, tailoring it to the unique dynamics of bradykinesia in PD. Statistical analysis of the extracted features, including those representing arrest and fatigue, has demonstrated their statistical significance in most cases. This finding underscores the importance of considering "occasional arrest" and "decrement in amplitude" in bradykinesia quantification of limb movement. Our enhanced diagnostic tool has been rigorously tested on an extensive dataset comprising 1396 motion videos from 310 PD patients, achieving an accuracy of 80.3%. The results confirm the robustness and reliability of our method.

Asymptotically Near-Optimal Hybrid Beamforming for mmWave IRS-Aided MIMO Systems

Hybrid beamforming is an emerging technology for massive multiple-input multiple-output (MIMO) systems due to the advantages of lower complexity, cost, and power consumption. Recently, intelligent reflection surface (IRS) has been proposed as the cost-effective technique for robust millimeter-wave (mmWave) MIMO systems. Thus, it is required to jointly optimize a reflection vector and hybrid beamforming matrices for IRS-aided mmWave MIMO systems. Due to the lack of RF chain in the IRS, it is unavailable to acquire the TX-IRS and IRS-RX channels separately. Instead, there are efficient methods to estimate the so-called effective (or cascaded) channel in literature. We for the first time derive the near-optimal solution of the aforementioned joint optimization only using the effective channel. Based on our theoretical analysis, we develop the practical reflection vector and hybrid beamforming matrices by projecting the asymptotic solution into the modulus constraint. Via simulations, it is demonstrated that the proposed construction can outperform the state-of-the-art (SOTA) method, where the latter even requires the knowledge of the TX-IRS ad IRS-RX channels separately. Furthermore, our construction can provide the robustness for channel estimation errors, which is inevitable for practical massive MIMO systems.

Channel Estimation for Reconfigurable Intelligent Surface Aided mmWave MU-MIMO Systems : Hybrid Receiver Architectures

Channel estimation is one of the key challenges for the deployment of reconfigurable intelligence surface (RIS)-aided communication systems. In this paper, we study the channel estimation problem of RIS-aided mmWave multi-user multiple-input multiple-output (MU-MIMO) systems especially when a hybrid receiver architecture is adopted. For this system, we propose a simple yet efficient channel estimation method using the fact that cascaded channels (to be estimated) have low-dimensional common column space. In the proposed method, the reflection vectors at the RIS and the RF combining matrices at the BS are designed such that the training observations are suitable for estimating the common column space and the user-specific coefficient matrices via a collaborative low-rank approximation. Via simulations, we demonstrate the effectiveness of the proposed channel estimation method compared with the state-of-the-art ones.