A contactless monitoring system for accurately predicting energy expenditure during treadmill walking based on an ensemble neural network.

The monitoring of treadmill walking energy expenditure (EE) plays an important role in health evaluations and management, particularly in older individuals and those with chronic diseases. However, universal and highly accurate prediction methods for walking EE are still lacking. In this paper, we propose an ensemble neural network (ENN) model that predicts the treadmill walking EE of younger and older adults and stroke survivors with high precision based on easy-to-obtain features. Compared with previous studies, the proposed model reduced the estimation error by 13.95% and 66.20% for stroke survivors and younger adults, respectively. Furthermore, a contactless monitoring system was developed based on Kinect, mm-wave radar, and ENN algorithms, and the treadmill walking EE was monitored in real time. This ENN model and monitoring system can be combined with smart devices and treadmill, making them suitable for evaluating, monitoring, and tracking changes in health during exercise and in rehabilitation environments.

Improved Parkinsonian tremor quantification based on automatic label modification and SVM with RBF kernel.

Objective. The quantitative assessment of Parkinsonian tremor, e.g. (0, 1, 2, 3, 4) according to the Movement Disorder Society-Unified Parkinson's Disease Rating Scale, is crucial for treating Parkinson's disease. However, the tremor amplitude constantly fluctuates due to environmental and psychological effects on the patient. In clinical practice, clinicians assess the tremor severity for a short duration, whereas manual tremor labeling relies on the clinician's physician experience. Therefore, automatic tremor quantification based on wearable inertial sensors and machine learning algorithms is affected by the manual labels of clinicians. In this study, an automatic modification method for the labels judged by clinicians is presented to improve Parkinsonian tremor quantitation.Approach. For the severe overlapping of dynamic feature range between different severities, an outlier modification algorithm (PCA-IQR) based on the combination of principal component analysis and interquartile range statistic rule is proposed to learn the blurred borders between different severity scores, thereby optimizing the labels. Afterward, according to the modified feature vectors, a support vector machine (SVM) with a radial basis function (RBF) kernel is proposed to classify the tremor severity. The classifier models of SVM with RBF kernel,k-nearest neighbors, and SVM with the linear kernel are compared.Main results. Experimental results show that the proposed method has high classification performance and excellent model generalization ability for tremor quantitation (accuracy: 97.93%, precision: 97.96%, sensitivity: 97.93%, F1-score: 97.94%).Significance. The proposed method may not only provide valuable assistance for clinicians to assess the tremor severity accurately, but also provides self-monitoring for patients at home and improve the assessment skills of clinicians.

Shape programmable magnetic pixel soft robot.

Magnetic soft robots (MSRs) can achieve controllable shape-morphing by magnetic programming to the magnetic elastomer. However, the magnetization profile is usually implemented on a continuous region and is unchangeable. The deformation and function design of MSR hence is limited. This study presents a programmable magnetic pixel soft robot (MPSR). By encapsulating liquid-metal/NdFeB composites into a Silicone shell, the thermal-magnetic response functional film with lattice-structure is fabricated, with the highest pixel resolution of 1 × 1 mm2. A piece of laser-assisted magnetic programming equipment is developed to implement magnetic encoding on discrete regions of the film. Therefore, a mathematical model is proposed to help calculate the magnetic codes according to the preset end shape. At last, several pixel-structure MPSRs are prepared and tested. Experimental results show that using the magnetic encoding technique, we can reconfigure the deformations and functions of the robot. This study provides a basis for the programmed shape regulation and motion design of the soft robot.

Validation of Inertial Sensing-Based Wearable Device for Tremor and Bradykinesia Quantification.

Neurologists judge the severity of Parkinsonian motor symptoms according to clinical scales, and their judgments exist inconsistent because of differences in clinical experience. Correspondingly, inertial sensing-based wearable devices (ISWDs) produce objective and standardized quantifications. However, ISWDs indirectly quantify symptoms by parametric modeling of angular velocities and linear accelerations nd trained by the judgments of several neurologists through supervised learning algorithms. Hence, the ISWD outputs are biased along with the scores provided by neurologists. To investigate the effectiveness ISWDs for Parkinsonian symptoms quantification, technical verification and clinical validation of both tremor and bradykinesia quantification methods were carried out. A total of 45 Parkinson's disease patients and 30 healthy controls performed the tremor and finger-tapping tasks, which were tracked simultaneously by an ISWD and a 6-axis high-precision electromagnetic tracking system (EMTS). The Unified Parkinson's Disease Rating Scale (UPDRS) prescribed parameters obtained from the EMTS, which directly provides linear and rotational displacements, were compared with the scores provided by both the ISWD and seven neurologists. EMTS-based parameters were regarded as the ground truth and were employed to train several common machine learning (ML) algorithms, i.e., support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF) algorithms. Inconsistency among the scores provided by the neurologists was proven. Besides, the quantification performance (sensitivity, specificity, and accuracy) of the ISWD employed with ML algorithms were better than that of the neurologists. Furthermore, EMTS can be utilized to both modify the quantification algorithms of ISWDs and improve the assessment skills of young neurologists.

[A mechanical impedance-based measurement system for quantifying Parkinsonian rigidity].

At present the parkinsonian rigidity assessment depends on subjective judgment of neurologists according to their experience. This study presents a parkinsonian rigidity quantification system based on the electromechanical driving device and mechanical impedance measurement method. The quantification system applies the electromechanical driving device to perform the rigidity clinical assessment tasks (flexion-extension movements) in Parkinson's disease (PD) patients, which captures their motion and biomechanical information synchronously. Qualified rigidity features were obtained through statistical analysis method such as least-squares parameter estimation. By comparing the judgments from both the parkinsonian rigidity quantification system and neurologists, correlation analysis was performed to find the optimal quantitative feature. Clinical experiments showed that the mechanical impedance has the best correlation (Pearson correlation coefficient r = 0.872, P < 0.001) with the clinical unified Parkinson's disease rating scale (UPDRS) rigidity score. Results confirmed that this measurement system is capable of quantifying parkinsonian rigidity with advantages of simple operation and effective assessment. In addition, the mechanical impedance can be adopted to help doctors to diagnose and monitor parkinsonian rigidity objectively and accurately.

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.

Continuous quantitative monitoring of physical activity in Parkinson's disease patients by using wearable devices: a case-control study.

The objective of this study was to explore the feasibility of using wearable devices to quantitatively measure the daily activity in patients with Parkinson's disease (PD) and to monitor medication-induced motor fluctuations. In this case-controlled study, we used monitored daily movement function in 21 patients with Parkinson's disease and 20 healthy volunteers. We analyzed the exercise types and sleep duration in the two groups and evaluated the correlation between daily movement function and age, gender, education, disease duration, Hohn-Yahr stage, UPDRS-II score, UPDRS-III score, and levodopa dose. We also determined the amount of exercise performed by PD patients at 1 h after taking levodopa and at 1 h before the next dose. The type of activity, average speed, and sleep duration in patients were significantly lower in PD patients than in healthy controls (P < 0.05). One hour after taking levodopa, patients were significantly more active than 1 h before the next dose (P < 0.05).Correlation analysis showed that age, gender, education, disease duration, Hohn-Yahr stage, UPDRS-II and UPDRS-III scores, and dosage of levodopa do not correlate with the daily movement function (P > 0.05) in patients with Parkinson's disease. In the control group, age and education were associated with daily movement function (P < 0.05), while gender was unrelated (P > 0.05). Continuous monitoring of daily activity may be useful to reveal medication-induced motor fluctuations in Parkinson's disease. The daily movement function may depend on age and education, but not on other parameters.

Experimental measurement on movement of spiral-type capsule endoscope.

Wireless capsule endoscope achieved great success, however, the maneuvering of wireless capsule endoscope is challenging at present. A magnetic driving instrument, including two bar magnets, a stepper motor, a motor driver, a motor controller, and a power supplier, was developed to generate rotational magnetic fields. Permanent magnet ring, magnetized as S and N poles radially and mounted spiral structure on the surface, acted as a capsule. The maximum torque passing to the capsule, rotational synchronization of capsule and motor, and the translational speed of capsule, were measured in ex vivo porcine large intestine. The experimental results illustrate that the rotational movement of the spiral-type capsule in the intestine is feasible and the cost of the magnetic driving equipment is low. As a result, the solution is promising in the future controllability.

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.

Quantitative assessment of parkinsonian bradykinesia based on an inertial measurement unit.

BACKGROUND: As the most characteristic feature of Parkinson's disease (PD), bradykinesia (slowness of movement) affects all patients with Parkinson's disease and interferes with their daily activities. This study introduces a wearable bradykinesia assessment system whose core component is composed of an inertial measurement unit. METHODS: The system diagram and assessment task were defined in accordance with clinical requirements from neurologists. Based on hand grasping actions, calculations of hand grasping ranges and statistical methods of quantitatively assessing parkinsonian bradykinesia were presented. Seven control subjects and eight patients were tested with this system. RESULTS: Experimental results show that a calculated bradykinesia parameter (modified mean range, instead of mean and standard deviation of the grasp ranges) correlated well with the evaluations of a neurologist (Pearson's correlation coefficient r = -0.83, p < 0.001). CONCLUSIONS: The bradykinesia assessment system was tested on both health subjects and PD patients. The results show that this system has greater correlation with the evaluations by neurologists than other parkinsonian bradykinesia assessment systems. The modified mean range was verified as the major bradykinesia parameter (key indicator). This study is helpful to those who want to use consumer-grade inertial sensors for quantitative assessment of motor symptoms during treatment.

A portable system for quantitative assessment of parkinsonian rigidity.

Rigidity is one of the primary symptoms of Parkinson's disease. Passive flexion and extension of the elbow is used to assess rigidity in this study. An examiner flexes and extends the subject's 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 (Cohen's 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.

An improved magnetic localization and orientation algorithm for wireless capsule endoscope.

In this paper, we propose a novel localization algorithm for tracking a magnet inside the capsule endoscope by 3-axis magnetic sensors array. In the algorithm, we first use an improved linear algorithm to obtain the localization parameters by finding the eigenvector corresponding to the minimum eigenvalue of the objective matrix. These parameters are used as the initial guess of the localization parameters in the nonlinear localization algorithm, and the nonlinear algorithm searches for more appropriate parameters that can minimize the objective error function. As the results, we obtain more robust and accurate localization results than those by using linear algorithm only. Nevertheless, the time efficiency of the nonlinear algorithm is enhanced. The real experimental data show that the average localization accuracy is about 2mm and the average orientation accuracy is about 1.6 degrees when the magnet moves within the sensing area of 240 mm x 240 mm square.