A Parkinson's disease measurement system using laser lines and a CMOS image sensor

Effective Mean Square Differences: A Matching Algorithm for Highly Similar Sheet Metal Parts

The accurate identification of highly similar sheet metal parts remains a challenging issue in sheet metal production. To solve this problem, this paper proposes an effective mean square differences (EMSD) algorithm that can effectively distinguish highly similar parts with high accuracy. First, multi-level downsampling and rotation searching are adopted to construct an image pyramid. Then, non-maximum suppression is utilised to determine the optimal rotation for each layer. In the matching, by re-evaluating the contribution of the difference between the corresponding pixels, the matching weight is determined according to the correlation between the grey value information of the matching pixels, and then the effective matching coefficient is determined. Finally, the proposed effective matching coefficient is adopted to obtain the final matching result. The results illustrate that this algorithm exhibits a strong discriminative ability for highly similar parts, with an accuracy of 97.1%, which is 11.5% higher than that of the traditional methods. It has excellent potential for application and can significantly improve sheet metal production efficiency.

A non-contact system for the assessment of hand motor tasks in people with Parkinson’s disease

Clinical diagnosis of Parkinson’s disease (PD) motor symptoms remains a problem. Most of the current studies focus on objective evaluations to make the evaluation more reliable. Most of these systems are based on the use of inertial and electromyographic sensors that require contact with the body part being assessed. Contact sensors restrict natural movement, may be uncomfortable and may require preparation of the body, which may cause irritation. As an alternative to contact sensors for the study of hand motor tasks performed by subjects with and without PD, electrical potential sensing technology is used in this research. A custom hardware has been designed to enable data collection by hand movement. A micro-machine system validated the developed system, and a relationship model was established between hand displacement and non-contact capacitive (NCC) sensor response. An experiment was conducted, including 57 subjects, 30 with PD (experimental group) and 27 healthy control group, followed by an analysis of statistical features extracted from the instantaneous mean frequency (IMNF) of NCC sensor. These results were compared with those obtained from gyroscope signals that are considered in the field to be the gold standard. As a result, NCC responses were correlated linearly with hand displacement (R2 = 0.7692 and $${\text{R}}_{\text{adj}}^{2}$$ R adj 2  = 0.7631). The statistical evaluation of IMNF features showed, that both, contact and non-contact sensors, were able to discriminate movement patterns of the control group from the experimental one. The results confirm statistical similarity between features extracted from NCC and gyroscope signals.

Application of automatized 3D moiré monitoring system in pulse measurement

This paper presents a quick monitor method to measure micro height variations directly. Here we apply optical moiré technology with a program designed by ourselves to Traditional Chinese Medical Pulse diagnosis. We analyze the moiré pattern which records the information of pulse, and then examine the conditions of pulse, the conditions of pulse including the location of pulse (by image processing the moiré pattern), the rhythm of pulse (via the frequency), the shape of pulse (via moiré pattern) and the strength of pulse (via amplitude intensity). Therefore, we can quantify the conditions of pulse by the system.

A non-contact pulse automatic positioning measurement system for traditional Chinese medicine

This study is to construct a non-contact pulse automatic positioning measurement system for Traditional Chinese Medicine (TCM) using optical triangulation measurements. The system consists of a linear laser, a CMOS image sensor and image analysis software. The linear laser is projected on the pulse beat location on the wrists; the CMOS image sensor records the process and the software analyzes the images. The program mainly uses the optical centroid and fast Fourier transform (FFT) principles to calculate centroid changes (pulse amplitude changes) from the images taken by the CMOS image sensor. It returns the positions of cun, guan and chi pulses automatically in terms of the amplitudes and the signals are then transformed from the time domain (time-amplitude) into the frequency domain (frequency-amplitude) via FFT to obtain the waveforms and frequencies of the cun, guan and chi pulses. It successfully extracts the data from the TCM pulse reading and can be a medical aid system for TCM. Combining the advantages of optical measurement and computer automation, this system provides a non-contact, easy to operate, fast in detection and low-cost equipment design.

DIMETER: a haptic master device for tremor diagnosis in neurodegenerative diseases

In this study, a device based on patient motion capture is developed for the reliable and non-invasive diagnosis of neurodegenerative diseases. The primary objective of this study is the classification of differential diagnosis between Parkinson's disease (PD) and essential tremor (ET). The DIMETER system has been used in the diagnoses of a significant number of patients at two medical centers in Spain. Research studies on classification have primarily focused on the use of well-known and reliable diagnosis criteria developed by qualified personnel. Here, we first present a literature review of the methods used to detect and evaluate tremor; then, we describe the DIMETER device in terms of the software and hardware used and the battery of tests developed to obtain the best diagnoses. All of the tests are classified and described in terms of the characteristics of the data obtained. A list of parameters obtained from the tests is provided, and the results obtained using multilayer perceptron (MLP) neural networks are presented and analyzed.

Characterization of a low-cost optical flow sensor when using an external laser as a direct illumination source

In this paper, a low cost optical flow sensor is combined with an external laser device to measure surface displacements and mechanical oscillations. The measurement system is based on applying coherent light to a diffuser surface and using an optical flow sensor to analyze the reflected and transferred light to estimate the displacement of the surface or the laser spot. This work is focused on the characterization of this measurement system, which can have the optical flow sensor placed at different angles and distances from the diffuser surface. The results have shown that the displacement of the diffuser surface is badly estimated when the optical mouse sensor is placed in front of the diffuser surface (angular orientation >150°) while the highest sensitivity is obtained when the sensor is located behind the diffuser surface and on the axis of the laser source (angular orientation 0°). In this case, the coefficient of determination of the measured displacement, R(2), was very high (>0.99) with a relative error of less than 1.29%. Increasing the distance between the surface and the sensor also increased the sensitivity which increases linearly, R(2) = 0.99. Finally, this measurement setup was proposed to measure very low frequency mechanical oscillations applied to the laser device, up to 0.01 Hz in this work. The results have shown that increasing the distance between the surface and the optical flow sensor also increases the sensitivity and the measurement range.