Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis

<p>Abstract</p> <p>Background</p> <p>The computer-aided identification of specific gait patterns is an important issue in the assessment of Parkinson's disease (PD). In this study, a computer vision-based gait analysis approach is developed to assist the clinical assessments of PD with kernel-based principal component analysis (KPCA).</p> <p>Method</p> <p>Twelve PD patients and twelve healthy adults with no neurological history or motor disorders within the past six months were recruited and separated according to their "Non-PD", "Drug-On", and "Drug-Off" states. The participants were asked to wear light-colored clothing and perform three walking trials through a corridor decorated with a navy curtain at their natural pace. The participants' gait performance during the steady-state walking period was captured by a digital camera for gait analysis. The collected walking image frames were then transformed into binary silhouettes for noise reduction and compression. Using the developed KPCA-based method, the features within the binary silhouettes can be extracted to quantitatively determine the gait cycle time, stride length, walking velocity, and cadence.</p> <p>Results and Discussion</p> <p>The KPCA-based method uses a feature-extraction approach, which was verified to be more effective than traditional image area and principal component analysis (PCA) approaches in classifying "Non-PD" controls and "Drug-Off/On" PD patients. Encouragingly, this method has a high accuracy rate, 80.51%, for recognizing different gaits. Quantitative gait parameters are obtained, and the power spectrums of the patients' gaits are analyzed. We show that that the slow and irregular actions of PD patients during walking tend to transfer some of the power from the main lobe frequency to a lower frequency band. Our results indicate the feasibility of using gait performance to evaluate the motor function of patients with PD.</p> <p>Conclusion</p> <p>This KPCA-based method requires only a digital camera and a decorated corridor setup. The ease of use and installation of the current method provides clinicians and researchers a low cost solution to monitor the progression of and the treatment to PD. In summary, the proposed method provides an alternative to perform gait analysis for patients with PD.</p

Current Laser Treatments for Benign Prostatic Hyperplasia

The latest technical improvements in the surgical armamentarium are remarkable. In particular, advancements in the urologic field are so exceptional that we could observe the flare-up of robot-assisted laparoscopic radical prostatectomy for prostate cancer and laser prostatectomy for benign prostatic hyperplasia (BPH). Photoselective vaporization of the prostate (PVP) and holmium laser prostatectomy are the most generalized options for laser surgery of BPH, and both modalities have shown good postoperative results. In comparison to transurethral prostatectomy (TURP), they showed similar efficacy and a much lower complication rate in randomized prospective clinical trials. Even in cases of large prostates, laser prostatectomy showed comparable efficacy and safety profiles compared to open prostatectomy. From a technical point of view, PVP is considered to be an easier technique for the urologist to master. Furthermore, patients can be safely followed up in an outpatient clinic. Holmium laser enucleation of the prostate (HoLEP) mimics open prostatectomy because the adenomatous tissue is peeled off the surgical capsule in both procedures. Therefore, HoLEP shows notable volume reduction of the prostate similar to open prostatectomy with fewer blood transfusions, shorter hospital stay, and cost reduction regardless of prostate size. Outcomes of laser prostatectomy for BPH are encouraging but sometimes are unbalanced because safety and feasibility studies were reported mainly for PVP, whereas long-term data are mostly available for HoLEP. We need longer-term randomized clinical data to identify the reoperation rate of PVP and to determine which procedure is the ideal alternative to TURP and open prostatectomy for each patient

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

A moment technique for suboptimal adaptive nonlinear filtering

Ph.D.James R. Rowlan