Houde Dai

A Six-Dimensional Magnetic Localization Algorithm for a Rectangular Magnet Objective Based on a Particle Swarm Optimizer

To build a wireless capsule endoscope with active external guidance for controllable and interactive diagnosis on the gastrointestinal tract, it is necessary to track the capsules 3-D position and 3-D orientation. An approach to tracking is to enclose a small rectangular permanent magnet in the capsule. The magnetic field produced around the body by the rectangular magnet can be detected by magnetic sensors outside the patients body. With these detected magnetic sensor data, the 3-D localization and 3-D orientation parameters can be computed by an appropriate algorithm based on the mathematical model of the rectangular magnets magnetic field. We tried several nonlinear optimization algorithms, and simulation experiments show that the particle swarm optimization algorithm can work effectively with good accuracy when the magnet moves within a predetermined range.

Quantitative Assessment of Parkinsonian Tremor Based on an Inertial Measurement Unit.

Quantitative assessment of parkinsonian tremor based on inertial sensors can provide reliable feedback on the effect of medication. In this regard, the features of parkinsonian tremor and its unique properties such as motor fluctuations and dyskinesia are taken into account. Least-square-estimation models are used to assess the severities of rest, postural, and action tremors. In addition, a time-frequency signal analysis algorithm for tremor state detection was also included in the tremor assessment method. This inertial sensor-based method was verified through comparison with an electromagnetic motion tracking system. Seven Parkinson’s disease (PD) patients were tested using this tremor assessment system. The measured tremor amplitudes correlated well with the judgments of a neurologist (r = 0.98). The systematic analysis of sensor-based tremor quantification and the corresponding experiments could be of great help in monitoring the severity of parkinsonian tremor.

A portable system for quantitative assessment of parkinsonian bradykinesia during deep-brain stimulation surgery

Deep-brain stimulation is the most effective surgical treatment for severe Parkinsons disease (PD). Bradykinesia is one of the primary symptoms of PD. 10 s hand grasping movement is used to assess bradykinesia severity in this study. An inertial measurement unit (IMU), which is attached to the middle finger, is used to measure the angular displacement of the middle finger movement during bradykinesia assessment task. The dominant grasping frequency, mean value and standard deviation (SD) of hand grasping ranges are used as the severity features of bradykinesia. Three healthy subjects and four PD patients were tested by the wearable system. The modified mean range correlated well with the 5-point clinical ratings. Further clinical experiments will be performed in the near future.

Quantitative assessment of tremor during deep brain stimulation using a wearable glove system

Deep brain stimulation (DBS) is a crucial surgical procedure for Parkinsons disease and essential tremor. There is yet no designated system for the accurate monitoring of the stimulating effect. Tremor is prominent in the Parkinsons disease. A novel wearable glove system for tremor quantification during DBS is presented. Rest, postural and action tremor assessment tasks are chosen as the feedback to the DBS treatment. Each tremor assessment task lasts for 10 seconds. A total of 5 patients with tremor were tested with the first prototype. Time-frequency analysis and statistical analysis were realized based on the inertial measurement unit signals. Results indicate that the tremor frequency was stable for all patients; however, the tremor amplitude fluctuated all the time. Valid state detection algorithm was performed for each assessment task. The mean tremor amplitudes of valid rest and postural tremor tasks correlate well with the clinical scores. After further experiments and verifications, this system is supposed to support the choosing of the optimal target location and stimulation intensity setting of the DBS electrode during DBS surgery.

A portable system for quantitative assessment of parkinsonian rigidity

Rigidity is one of the primary symptoms of Parkinsons disease. Passive flexion and extension of the elbow is used to assess rigidity in this study. An examiner flexes and extends the subjects elbow joint through a rigidity assessment cuff attached around the wrist. Each assessment lasts for 10 seconds. Two force sensor boxes and an inertial measurement unit are used to measure the applied force and the state of the elbow movement. Elastic and viscous values will be obtained through a least squares estimation with all the data. 9 healthy subjects were tested with this system in two experimental conditions: 1) normal state (relaxed); 2) imitated rigidity state. Also the subjects were performed the assessment task with different frequencies and elbow movement ranges. The imitated rigidity action increases viscosity and elasticity. The effect sizes (Cohens d) of the viscosity and elasticity between normal state and imitated state are 1.61 and 1.36 respectively, which means the difference is significant. Thus, this system can detect the on-off fluctuations of parkinsonian rigidity. Both wrist movement angle and frequency have small effect on the viscosity, but have elevated effect on the elasticity.

Control system design for a continuous positive airway pressure ventilator

Continuous Positive Airway Pressure (CPAP) ventilation remains a mainstay treatment for obstructive sleep apnea syndrome (OSAS). Good pressure stability and pressure reduction during exhalation are of major importance to ensure clinical efficacy and comfort of CPAP therapy. In this study an experimental CPAP ventilator was constructed using an application-specific CPAP blower/motor assembly and a microprocessor. To minimize pressure variations caused by spontaneous breathing as well as the uncomfortable feeling of exhaling against positive pressure, we developed a composite control approach including the feed forward compensator and feedback proportional-integral-derivative (PID) compensator to regulate the pressure delivered to OSAS patients. The Ziegler and Nichols method was used to tune PID controller parameters. And then we used a gas flow analyzer (VT PLUS HF) to test pressure curves, flow curves and pressure-volume loops for the proposed CPAP ventilator. The results showed that it met technical criteria for sleep apnea breathing therapy equipment. Finally, the study made a quantitative comparison of pressure stability between the experimental CPAP ventilator and commercially available CPAP devices.

Continuous blood pressure monitor with wireless interface

This paper gives an overview of a project, which is focused on the development of a convenient device for continuous blood pressure (BP) monitoring with wireless communication interface. The reliability of long-term automatically monitoring is the main focus for current paper. 18 healthy subjects were tested with the continuous BP monitor against a brand of community-based BP monitor. Accuracy assessment of the monitor has been accomplished. As a result of current study, it can be used to self-monitoring in home.

Comparison of methods for tremor frequency analysis for patients with Parkinson's disease

Tremor is described as involuntary rhythmic oscillations of one or more body parts. It is a symptom of Parkinsons disease (PD). The severity of tremor is based on its frequency. Using acceleration sensors, one can detect tremor of the limbs or other body parts. Data from sensors can be processed using spectral analysis. The most common methods for the investigation of tremor are Fast Fourier Transformation (FFT), Short Time Fourier Transform (STFT) and power spectral density analysis (PSD). In this paper we investigate these methods together with peak detection and pattern recognition methods. We compare the various approaches with each other with respect to frequency. A visual frequency analysis using an optical tracking system is used as a reference. The experiments were performed with a measuring glove with integrated acceleration sensors on the middle finger and thumb joint. We examined the accuracy of the various methods for the analysis of tremor in PD patients.

An Improved Calibration Method for a Rotating 2D LIDAR System

This paper presents an improved calibration method of a rotating two-dimensional light detection and ranging (R2D-LIDAR) system, which can obtain the 3D scanning map of the surroundings. The proposed R2D-LIDAR system, composed of a 2D LIDAR and a rotating unit, is pervasively used in the field of robotics owing to its low cost and dense scanning data. Nevertheless, the R2D-LIDAR system must be calibrated before building the geometric model because there are assembled deviation and abrasion between the 2D LIDAR and the rotating unit. Hence, the calibration procedures should contain both the adjustment between the two devices and the bias of 2D LIDAR itself. The main purpose of this work is to resolve the 2D LIDAR bias issue with a flat plane based on the Levenberg–Marquardt (LM) algorithm. Experimental results for the calibration of the R2D-LIDAR system prove the reliability of this strategy to accurately estimate sensor offsets with the error range from −15 mm to 15 mm for the performance of capturing scans.

Optimization for a Neural Network Based on Input-Vectors Correlation and Its Application to a Truck Scale

It is important and valuable to improve the generalization ability of a neural network (NN) when there is a lack of training samples. This paper presents an approach of optimizing a NN based on input-vectors correlation (IVCNN) to improve the NNs ability in the case of lacking samples, and then uses this method to compensate for the weighing errors of a truck scale. First, we analyze the truck scales weighing principle and the spatial correlation of the truck scales input signals, and then use this correlation to construct the constraint conditions and the performance index for training an NN. Finally, we give the detailed training algorithm of IVCNN. In addition, we prove the IVCNNs convergence and analyze its anti-interference performance, convergent speed, and computational complexity. The experimental results demonstrate the effectiveness of IVCNN by comparison with an NN based on the data induction method (DINN, i.e., an NN trained only by data samples, not prior knowledge) and supported vector regression.