Diagnosis of neurodegenerative diseases with a refined Lempel-Ziv complexity.

The investigation into the distinctive difference of gait is of significance for the clinical diagnosis of neurodegenerative diseases. However, human gait is affected by many factors like behavior, occupation and so on, and they may confuse the gait differences among Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease. For the purpose of examining distinctive gait differences of neurodegenerative diseases, this study extracts various features from both vertical ground reaction force and time intervals. Moreover, refined Lempel-Ziv complexity is proposed considering the detailed distribution of signals based on the median and quartiles. Basic features (mean, coefficient of variance, and the asymmetry index), nonlinear dynamic features (Hurst exponent, correlation dimension, largest Lyapunov exponent), and refined Lempel-Ziv complexity of different neurodegenerative diseases are compared statistically by violin plot and Kruskal-Wallis test to reveal distinction and regularities. The comparative analysis results illustrate the gait differences across these neurodegenerative diseases by basic features and nonlinear dynamic features. Classification results by random forest indicate that the refined Lempel-Ziv complexity can robustly enhance the diagnosis accuracy when combined with basic features.

Diffusion and structural MRI as potential biomarkers in people with Parkinson's disease and cognitive impairment.

OBJECTIVE: To explore the neuroimage change in Parkinson's disease (PD) patients with cognitive impairments, this study investigated the correlation between plasma biomarkers and morphological brain changes in patients with normal cognition and mild cognitive impairment. The objective was to identify the potential target deposition regions of the plasma biomarkers and to search for the relevant early neuroimaging biomarkers on the basis of different cognitive domains. METHODS: Structural brain MRI and diffusion weighted images were analyzed from 49 eligible PD participants (male/female: 27/22; mean age: 73.4 ± 8.5 years) from a retrospective analysis. Plasma levels of α-synuclein, amyloid beta peptide, and total tau were collected. A comprehensive neuropsychological assessment of the general and specific cognitive domains was performed. Difference between PD patients with normal cognition and impairment was examined. Regression analysis was performed to evaluate the correlation between image-derived index and plasma biomarkers or neuropsychological assessments. RESULTS: Significant correlation was found between plasma Aß-42 level and fractional anisotropy of the middle occipital, angular, and middle temporal gyri of the left brain, as well as plasma T-tau level and the surface area of the isthmus or the average thickness of the posterior part of right cingulate gyrus. Visuospatial and executive function is positively correlated with axial diffusivity in bilateral cingulate gyri. CONCLUSION: In nondemented PD patients, the target regions for plasma deposition might be located in the cingulate, middle occipital, angular, and middle temporal gyri. Changes from multiple brain regions can be correlated to the performance of different cognitive domains. CLINICAL RELEVANCE STATEMENT: Cognitive impairment in Parkinson's disease is primarily linked to biomarkers associated with Alzheimer's disease rather than those related to Parkinson's disease and resembles the frontal variant of Alzheimer's disease, which may guide management strategies for cognitive impairment in Parkinson's disease. KEY POINTS: • Fractional anisotropy, surface area, and thickness in the cingulate, middle occipital, angular, and middle temporal gyri can be significantly correlated with plasma Aß-42 and T-tau level. • Axial diffusivity in the cingulate gyri was correlated with visuospatial and executive function. • The pattern of cognitive impairment in Parkinson's disease can be similar to the frontal variant than typical Alzheimer's disease.

Wearable multisource quantitative gait analysis of Parkinson's diseases.

As the motor symptoms of Parkinson's disease (PD) are complex and influenced by many factors, it is challenging to quantify gait abnormalities adequately using a single type of signal. Therefore, a wearable multisource gait monitoring system is developed to perform a quantitative analysis of gait abnormalities for improving the effectiveness of the clinical diagnosis. To detect multisource gait data for an accurate evaluation of gait abnormalities, force sensitive sensors, piezoelectric sensors, and inertial measurement units are integrated into the devised device. The modulation circuits and wireless framework are designed to simultaneously collect plantar pressure, dynamic deformation, and postural angle of the foot and then wirelessly transmit these collected data. With the designed system, multisource gait data from PD patients and healthy controls are collected. Multisource features for quantifying gait abnormalities are extracted and evaluated by a significance test of difference and correlation analysis. The results show that the features extracted from every single type of data are able to quantify the health status of the subjects (p < 0.001, ρ > 0.50). More importantly, the validity of multisource gait data is verified. The results demonstrate that the gait feature fusing multisource data achieves a maximum correlation coefficient of 0.831, a maximum Area Under Curve of 0.9206, and a maximum feature-based classification accuracy of 88.3%. The system proposed in this study can be applied to the gait analysis and objective evaluation of PD.

Non-human primate models and systems for gait and neurophysiological analysis.

Brain-computer interfaces (BCIs) have garnered extensive interest and become a groundbreaking technology to restore movement, tactile sense, and communication in patients. Prior to their use in human subjects, clinical BCIs require rigorous validation and verification (V&V). Non-human primates (NHPs) are often considered the ultimate and widely used animal model for neuroscience studies, including BCIs V&V, due to their proximity to humans. This literature review summarizes 94 NHP gait analysis studies until 1 June, 2022, including seven BCI-oriented studies. Due to technological limitations, most of these studies used wired neural recordings to access electrophysiological data. However, wireless neural recording systems for NHPs enabled neuroscience research in humans, and many on NHP locomotion, while posing numerous technical challenges, such as signal quality, data throughout, working distance, size, and power constraint, that have yet to be overcome. Besides neurological data, motion capture (MoCap) systems are usually required in BCI and gait studies to capture locomotion kinematics. However, current studies have exclusively relied on image processing-based MoCap systems, which have insufficient accuracy (error: ≥4° and 9 mm). While the role of the motor cortex during locomotion is still unclear and worth further exploration, future BCI and gait studies require simultaneous, high-speed, accurate neurophysiological, and movement measures. Therefore, the infrared MoCap system which has high accuracy and speed, together with a high spatiotemporal resolution neural recording system, may expand the scope and improve the quality of the motor and neurophysiological analysis in NHPs.

Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review.

Objective: Neurodegenerative diseases affect millions of families around the world, while various wearable sensors and corresponding data analysis can be of great support for clinical diagnosis and health assessment. This systematic review aims to provide a comprehensive overview of the existing research that uses wearable sensors and features for the diagnosis of neurodegenerative diseases. Methods: A systematic review was conducted of studies published between 2015 and 2022 in major scientific databases such as Web of Science, Google Scholar, PubMed, and Scopes. The obtained studies were analyzed and organized into the process of diagnosis: wearable sensors, feature extraction, and feature selection. Results: The search led to 171 eligible studies included in this overview. Wearable sensors such as force sensors, inertial sensors, electromyography, electroencephalography, acoustic sensors, optical fiber sensors, and global positioning systems were employed to monitor and diagnose neurodegenerative diseases. Various features including physical features, statistical features, nonlinear features, and features from the network can be extracted from these wearable sensors, and the alteration of features toward neurodegenerative diseases was illustrated. Moreover, different kinds of feature selection methods such as filter, wrapper, and embedded methods help to find the distinctive indicator of the diseases and benefit to a better diagnosis performance. Conclusions: This systematic review enables a comprehensive understanding of wearable sensors and features for the diagnosis of neurodegenerative diseases.

An aerosol deposition based MEMS piezoelectric accelerometer for low noise measurement.

Potentially applied in low-noise applications such as structural health monitoring (SHM), a 1-axis piezoelectric MEMS accelerometer based on aerosol deposition is designed, fabricated, simulated, and measured in this study. It is a cantilever beam structure with a tip proof mass and PZT sensing layer. To figure out whether the design is suitable for SHM, working bandwidth and noise level are obtained via simulation. For the first time, we use aerosol deposition method to deposit thick PZT film during the fabrication process to achieve high sensitivity. In performance measurement, we obtain the charge sensitivity, natural frequency, working bandwidth and noise equivalent acceleration of 22.74 pC/g, 867.4 Hz, 10-200 Hz (within ±5% deviation) and 5.6 µ g / Hz (at 20 Hz). To demonstrate its feasibility for real applications, vibrations of a fan are measured by our designed sensor and a commercial piezoelectric accelerometer, and the results match well with each other. Moreover, shaker vibration measurement with ADXL1001 indicates that the fabricated sensor has a much lower noise level. In the end, we show that our designed accelerometer has good performance compared to piezoelectric MEMS accelerometers in relevant studies and great potential for low-noise applications compared to low-noise capacitive MEMS accelerometers.

Simultaneous Estimation of the Vertical Stiffness in the Knee and Hip for Healthy Human Subjects during Walking.

The stiffness of lower limb joints is a critical characteristic of walking. To investigate the potential of establishing a simple and universal model to describe the characteristics related to vertical vibration during human walking, vertical stiffness is introduced at the knee and hip. A multi-mass-spring model of the human body is established in the vertical direction. In the Fourier form, results of experiments on 14 healthy adults show that the vertical displacements of joints are a function of the leg length and walking cadence, while the ground reaction force is a function of the body weight and walking cadence. The obtained universal equations of vertical displacement and ground reaction force are employed as the input parameters to the proposed multi-mass-spring model. Thus, the vertical stiffness in the knee and hip can then be estimated simultaneously by the subject's weight, leg length, and walking cadence. The variation of vertical stiffness shows different time-varying trends in different gait phases across the entire gait cycle. Finally, the proposed model for vertical stiffness estimation is validated by the vertical oscillation of the pelvis. The average error across three gait cycles for all subjects is 20.48%, with a standard deviation of 5.44%. These results display that the vertical stiffness of knee and hip across the entire gait cycle can be directly estimated by individual parameters that are easy to measure. It provides a different view of human walking analysis and may be applied in future pathological gait recognition, bipedal robots, and lower limb exoskeletons.

Hybrid Therapeutic Device (CUHK-OA-M2) for Relieving Symptoms Induced by Knee Osteoarthritis.

The symptoms of knee osteoarthritis (KOA) severely affect the life quality of the elderly population. Low-level laser therapy, heat therapy, and massage therapy are widely used as independent treatments for joint disorders. However, there are very limited reports of a combination of these therapies into an integrated device for KOA so far. This study aims to develop a novel hybrid therapeutic device that can meet various requirements for knee therapy. Our hybrid therapeutic device (CUHK-OA-M2) integrated with low-level laser therapy, heat therapy, and local massage therapy can effectively provide patients with KOA with relief from their clinical symptoms. A pilot test of 50 community-dwelling elderly volunteers with KOA was performed. Finally, 43 volunteers completed two treatment periods (30 days each) and two post-treatment periods (30 days each). The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores were collected and analyzed after each period. The outputs of the low-level laser, heating, and massage therapies significantly decreased the WOMAC scores in terms of pain, stiffness, function and total WOMAC after two treatment periods (p < 0.05). Although the score increased slightly after the post-treatment period, it was still lower than the baseline, indicating the treatment outcome could last for an extended period. Therefore, our CUHK-OA-M2 device, as an integrated multi-functional hybrid therapeutic device, is therapeutically significant for treating osteoarthritis symptoms on the knee joints of elderly subjects.

Subclinical alterations of resting state functional brain network for adjunctive bright light therapy in nonseasonal major depressive disorder: A double blind randomized controlled trial.

Introduction: The treatment effect of bright light therapy (BLT) on major depressive disorder (MDD) has been proven, but the underlying mechanism remains unclear. Neuroimaging biomarkers regarding disease alterations in MDD and treatment response are rarely focused on BLT. This study aimed to identify the modulatory mechanism of BLT in MDD using resting-state functional magnetic resonance imaging (rfMRI). Materials and methods: This double-blind, randomized controlled clinical trial included a dim red light (dRL) control group and a BLT experimental group. All participants received light therapy for 30 min every morning for 4 weeks. The assessment of the Hamilton Depression Rating Scale-24 (HAMD-24) and brain MRI exam were performed at the baseline and the 4-week endpoint. The four networks in interest, including the default mode network (DMN), frontoparietal network (FPN), salience network (SN), and sensorimotor network (SMN), were analyzed. Between-group differences of the change in these four networks were evaluated. Results: There were 22 and 21 participants in the BLT and dRL groups, respectively. Age, sex, years of education, baseline severity, and improvement in depressive symptoms were not significantly different between the two groups. The baseline rfMRI data did not show any significant functional connectivity differences within the DMN, FPN, SN, and SMN between the two groups. Compared with the dRL group, the BTL group showed significantly increased functional connectivity after treatment within the DMN, FPN, SN, and SMN. Graph analysis of the BLT group demonstrated an enhancement of betweenness centrality and global efficiency. Conclusion: BLT can enhance intra-network functional connectivity in the DMN, FPN, SN, and SMN for MDD patients. Furthermore, BLT improves the information processing of the whole brain. Clinical trial registration: The ClinicalTrials.gov identifier was NCT03941301.

Gait Synergy Analysis and Modeling on Amputees and Stroke Patients for Lower Limb Assistive Devices.

The concept of synergy has drawn attention and been applied to lower limb assistive devices such as exoskeletons and prostheses for improving human-machine interaction. A better understanding of the influence of gait kinematics on synergies and a better synergy-modeling method are important for device design and improvement. To this end, gait data from healthy, amputee, and stroke subjects were collected. First, continuous relative phase (CRP) was used to quantify their synergies and explore the influence of kinematics. Second, long short-term memory (LSTM) and principal component analysis (PCA) were adopted to model interlimb synergy and intralimb synergy, respectively. The results indicate that the limited hip and knee range of motions (RoMs) in stroke patients and amputees significantly influence their synergies in different ways. In interlimb synergy modeling, LSTM (RMSE: 0.798° (hip) and 1.963° (knee)) has lower errors than PCA (RMSE: 5.050° (hip) and 10.353° (knee)), which is frequently used in the literature. Further, in intralimb synergy modeling, LSTM (RMSE: 3.894°) enables better synergy modeling than PCA (RMSE: 10.312°). In conclusion, stroke patients and amputees perform different compensatory mechanisms to adapt to new interlimb and intralimb synergies different from healthy people. LSTM has better synergy modeling and shows a promise for generating trajectories in line with the wearer's motion for lower limb assistive devices.

Synergy-based knee angle estimation using kinematics of thigh.

BACKGROUND: Lower limb assistive devices have been developed to help amputees or stroke patients. To precisely mimic the required function, researchers focused on how to estimate/predict the required knee angle for knee devices. RESEARCH QUESTION: The objective is to estimate the motion of the human knee joint during walking using the kinematics of wearer's thigh measured by a single Inertial Measurement Unit (IMU). The hypotheses are that the proposed method can precisely estimate knee angle and have good universality on different subjects, speeds and strides. METHOD: 8 healthy subjects walked on the level ground at three different speeds. An IMU mounted on the thigh was employed to collect the kinematic information of the thigh including angular velocities and accelerations. A long short-term memory (LSTM) neural network model was adopted to model intra-limb synergy between the motion of thigh and the knee joint. Such that with the trained LSTM model, the knee angle can be precisely predicted. RESULTS: Compared with the existing studies, the proposed approach based on an LSTM model has better estimation performance. The average RMSE for eight subjects can be limited to 3.89°. The proposed method has speed and stride adaptability. SIGNIFICANCE: The proposed method is promising to generate a desired and harmonious knee trajectory in line with thigh motion for assistive robotic devices.

IMU-Based Locomotion Mode Identification for Transtibial Prostheses, Orthoses, and Exoskeletons.

Active transtibial prostheses, orthoses, and exoskeletons hold the promise of improving the mobility of lower-limb impaired or amputated individuals. Locomotion mode identification (LMI) is essential for these devices precisely reproducing the required function in different terrains. In this study, a terrain geometry-based LMI algorithm is proposed. The environment should be built according to the inclination grade of the ground. For example, when the inclination angle is between 7 degrees and 15 degrees, the environment should be a ramp. If the inclination angle is around 30 degrees, the environment is preferred to be equipped with stairs. Given that, the locomotion mode/terrain can be classified by the inclination grade. Besides, human feet always move along the surface of terrain to minimize the energy expenditure for transporting lower limbs and get the required foot clearance. Hence, the foot trajectory estimated by an IMU was used to derive the inclination grade of the terrain that we traverse to identify the locomotion mode. In addition, a novel trigger condition (an elliptical boundary) is proposed to activate the decision-making of the LMI algorithm before the next foot strike thus leaving enough time for performing preparatory work in the swing phase. When the estimated foot trajectory goes across the elliptical boundary, the decision-making will be executed. Experimental results show that the average accuracy for three healthy subjects and three below-knee amputees is 98.5% in five locomotion modes: level-ground walking, up slope, down slope, stair descent, and stair ascent. Besides, all the locomotion modes can be identified before the next foot strike.

Self-Powered Smart Insole for Monitoring Human Gait Signals.

With the rapid development of low-power consumption wireless sensors and wearable electronics, harvesting energy from human motion to enable self-powered sensing is becoming desirable. Herein, a pair of smart insoles integrated with piezoelectric poly(vinylidene fluoride) (PVDF) nanogenerators (NGs) are fabricated to simultaneously harvest energy from human motion and monitor human gait signals. Multi-target magnetron sputtering technology is applied to form the aluminum electrode layers on the surface of the PVDF film and the self-powered insoles are fabricated through advanced 3D seamless flat-bed knitting technology. Output responses of the NGs are measured at different motion speeds and a maximum value of 41 V is obtained, corresponding to an output power of 168.1 µW. By connecting one NG with an external circuit, the influence of external resistance, capacitor, and motion speed on the charging characteristics of the system is systematically investigated. To demonstrate the potential of the smart insoles for monitoring human gait signals, two subjects were asked to walk on a treadmill at different speeds or with a limp. The results show that one can clearly distinguish walking with a limp from regular slow, normal, and fast walking states by using multiscale entropy analysis of the stride intervals.

User-Adaptive Assistance of Assistive Knee Braces for Gait Rehabilitation.

Patients suffering from neurological and orthopedic diseases or injuries usually have mobility impairment problems, and they require customized rehabilitation training to recover. In recent years, robotic assistive devices have been widely studied for gait rehabilitation. In this paper, methods to determine user-adaptive assistance of assistive knee braces (AKBs) in gait rehabilitation are investigated. A fuzzy expert system, which takes a patient's physical condition and gait analysis results as inputs, is proposed to configure suitable levels of different assistive functions of the AKB. During gait rehabilitation, the AKB generates a reference knee trajectory according to the patient's individual gait pattern, and the interaction force is controlled through a hybrid impedance controller considering the individual assistive function configuration. The proposed methods are verified through clinical pilot studies of a patient with lower limb weakness. Experimental results show that AKB with the proposed control strategies can provide effective assistance to improve the patient's gait performance during gait rehabilitation.

Anticorrosive, Ultralight, and Flexible Carbon-Wrapped Metallic Nanowire Hybrid Sponges for Highly Efficient Electromagnetic Interference Shielding.

Metal-based materials with exceptional intrinsic conductivity own excellent electromagnetic interference (EMI) shielding performance. However, high density, corrosion susceptibility, and poor flexibility of the metal severely restrict their further applications in the areas of aircraft/aerospace, portable and wearable smart electronics. Herein, a lightweight, flexible, and anticorrosive silver nanowire wrapped carbon hybrid sponge (Ag@C) is fabricated and employed as ultrahigh efficiency EMI shielding material. The interconnected Ag@C hybrid sponges provide an effective way for electron transport, leading to a remarkable conductivity of 363.1 S m-1 and superb EMI shielding effectiveness of around 70.1 dB in the frequency range of 8.2-18 GHz, while the density is as low as 0.00382 g cm-3 , which are among the best performances for electrically conductive sponges/aerogels/foams by far. More importantly, the Ag@C sponge surprisingly exhibits super-hydrophobicity and strong corrosion resistance. In addition, the hybrid sponges possess excellent mechanical resilience even with a large strain (90% reversible compressibility) and an outstanding cycling stability, which is far better than the bare metallic aerogels, such as silver nanowire aerogels and copper nanowire foams. This strategy provides a facile methodology to fabricate lightweight, flexible, and anticorrosive metal-based sponge for highly efficient EMI shielding applications.

A wearable exoskeleton suit for motion assistance to paralysed patients.

BACKGROUND/OBJECTIVE: The number of patients paralysed due to stroke, spinal cord injury, or other related diseases is increasing. In order to improve the physical and mental health of these patients, robotic devices that can help them to regain the mobility to stand and walk are highly desirable. The aim of this study is to develop a wearable exoskeleton suit to help paralysed patients regain the ability to stand up/sit down (STS) and walk. METHODS: A lower extremity exoskeleton named CUHK-EXO was developed with considerations of ergonomics, user-friendly interface, safety, and comfort. The mechanical structure, human-machine interface, reference trajectories of the exoskeleton hip and knee joints, and control architecture of CUHK-EXO were designed. Clinical trials with a paralysed patient were performed to validate the effectiveness of the whole system design. RESULTS: With the assistance provided by CUHK-EXO, the paralysed patient was able to STS and walk. As designed, the actual joint angles of the exoskeleton well followed the designed reference trajectories, and assistive torques generated from the exoskeleton actuators were able to support the patient's STS and walking motions. CONCLUSION: The whole system design of CUHK-EXO is effective and can be optimised for clinical application. The exoskeleton can provide proper assistance in enabling paralysed patients to STS and walk.

Human Gait Modeling and Analysis Using a Semi-Markov Process With Ground Reaction Forces.

Modeling and evaluation of patients' gait patterns is the basis for both gait assessment and gait rehabilitation. This paper presents a convenient and real-time gait modeling, analysis, and evaluation method based on ground reaction forces (GRFs) measured by a pair of smart insoles. Gait states are defined based on the foot-ground contact forms of both legs. From the obtained gait state sequence and the duration of each state, the human gait is modeled as a semi-Markov process (SMP). Four groups of gait features derived from the SMP gait model are used for characterizing individual gait patterns. With this model, both the normal gaits of healthy people and the abnormal gaits of patients with impaired mobility are analyzed. Abnormal evaluation indices (AEI) are further proposed for gait abnormality assessment. Gait analysis experiments are conducted on 23 subjects with different ages and health conditions. The results show that gait patterns are successfully obtained and evaluated for normal, age-related, and pathological gaits. The effectiveness of the proposed AEI for gait assessment is verified through comparison with a video-based gait abnormality rating scale.

Recent developments and challenges of lower extremity exoskeletons.

The number of people with a mobility disorder caused by stroke, spinal cord injury, or other related diseases is increasing rapidly. To improve the quality of life of these people, devices that can assist them to regain the ability to walk are of great demand. Robotic devices that can release the burden of therapists and provide effective and repetitive gait training have been widely studied recently. By contrast, devices that can augment the physical abilities of able-bodied humans to enhance their performances in industrial and military work are needed as well. In the past decade, robotic assistive devices such as exoskeletons have undergone enormous progress, and some products have recently been commercialized. Exoskeletons are wearable robotic systems that integrate human intelligence and robot power. This paper first introduces the general concept of exoskeletons and reviews several typical lower extremity exoskeletons (LEEs) in three main applications (i.e. gait rehabilitation, human locomotion assistance, and human strength augmentation), and provides a systemic review on the acquisition of a wearer's motion intention and control strategies for LEEs. The limitations of the currently developed LEEs and future research and development directions of LEEs for wider applications are discussed.

Identification of ankle sprain motion from common sporting activities by dorsal foot kinematics data.

This study presented a method to identify ankle sprain motion from common sporting activities by dorsal foot kinematics data. Six male subjects performed 300 simulated supination sprain trials and 300 non-sprain trials in a laboratory. Eight motion sensors were attached to the right dorsal foot to collect three-dimensional linear acceleration and angular velocity kinematics data, which were used to train up a support vector machine (SVM) model for the identification purpose. Results suggested that the best identification method required only one motion sensor located at the medial calcaneus, and the method was verified on another group of six subjects performing 300 simulated supination sprain trials and 300 non-sprain trials. The accuracy of this method was 91.3%, and the method could help developing a mobile motion sensor system for ankle sprain detection.