Search Results (19)

Background: SPATA5L1-related neurodevelopmental disorder is a recently described condition characterized by psychomotor delay, sensorineural hearing loss, and variable motor dysfunction. Because only a few cases have been reported, the full phenotypic spectrum remains poorly defined. Expanding clinical characterization is crucial for improving early diagnosis and targeted management. Case Presentation: We report a 24-month-old female with compound heterozygous SPATA5L1 variants c.1918C>T (p.Arg640Ter) and c.2066G>T (p.Gly689Val). She presented with global psychomotor delay, bilateral sensorineural hearing loss, strabismus, and craniofacial dysmorphism. Brain MRI showed cortical and white matter atrophy, delayed myelination, and a thin corpus callosum. Vojta neurodevelopmental assessment demonstrated an 11-month motor delay, abnormal responses in all seven Vojta postural reactions, and persistent primitive reflexes. Early EEG recordings were without significant changes, whereas abnormalities emerged later in the clinical course. Genetic testing confirmed the variants in trans. Management and Outcomes: Early rehabilitation including reflex locomotion therapy was initiated. The persistence of primitive reflexes, central hypotonia, and pathological postural reactions provided a coherent neuromotor profile and indicated a high vulnerability to atypical motor development, and do not rule out the possibility of later evolution toward a spastic–dystonic motor pattern. These findings, combined with neuroimaging abnormalities, refined the patient’s neuromotor phenotype and guided individualized therapeutic planning. Conclusions: This case expands the clinical and neurodevelopmental spectrum associated with SPATA5L1 variants and highlights the diagnostic value of integrating genomic sequencing with structured motor assessments. Early, multidimensional evaluation may improve recognition of rare neurodevelopmental disorders and support more precise prognostication and rehabilitation strategies. Full article

Accurately decoding human locomotion intention from physiological signals remains a significant hurdle for the seamless control of advanced rehabilitation devices like exoskeletons and intelligent prosthetics. Conventional recognition methods often falter, exhibiting limited accuracy and struggling to capture the complex, nonlinear dynamics inherent in biological data streams. Addressing these critical limitations, this study introduces a novel framework for lower-limb motion intent recognition, integrating Kernel Principal Component Analysis (KPCA) with a Support Vector Machine (SVM) optimized via an Improved Sparrow Search Algorithm (ISSA). Our approach commences by constructing a comprehensive high-dimensional feature space from synchronized surface electromyography (sEMG) and inertial measurement unit (IMU) data—a potent combination reflecting both muscle activation and limb kinematics. Critically, KPCA is employed for nonlinear dimensionality reduction; leveraging the power of kernel functions, it transcends the linear constraints of traditional PCA to extract low-dimensional principal components that retain significantly more discriminative information. Furthermore, the Sparrow Search Algorithm (SSA) undergoes three strategic enhancements: chaotic opposition-based learning for superior population diversity, adaptive dynamic weighting to adeptly balance exploration and exploitation, and hybrid mutation strategies to effectively mitigate premature convergence. This enhanced ISSA meticulously optimizes the SVM hyperparameters, ensuring robust classification performance. Experimental validation, conducted on a challenging 13-class lower-limb motion dataset, compellingly demonstrates the superiority of the proposed KPCA-ISSA-SVM architecture. It achieves a remarkable recognition accuracy of 95.35% offline and 93.3% online, substantially outperforming conventional PCA-SVM (91.85%) and standalone SVM (89.76%) benchmarks. This work provides a robust and significantly more accurate solution for intention perception in human–machine systems, paving the way for more intuitive and effective rehabilitation technologies by adeptly handling the nonlinear coupling characteristics of sEMG-IMU data and complex motion patterns. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by the accumulation of β-amyloid (Aβ) and hyperphosphorylated tau, leading to neuroinflammation, oxidative stress, and neuronal death. Early detection of AD remains a challenge, as clinical manifestations only emerge in the advanced stages, limiting therapeutic interventions. Minimally invasive biomarkers are essential for early identification and monitoring of disease progression. This study aims to evaluate the sensitivity of the relationship between serum oligoelement levels as biomarkers and the monitoring of AD progression in the 3xTg-AD model. Transgenic 3xTg-AD mice and C57BL/6 controls were evaluated over 12 months through serum oligoelement quantification using inductively coupled plasma mass spectrometry (ICP-MS), Aβ deposition via immunohistochemistry, and cognitive assessments using memory tests (Morris water maze and novel object recognition test), as well as spontaneous locomotion analysis using the open field test. The results demonstrated that oligoelements (copper, zinc, and selenium) were sensitive in detecting alterations in the AD group, preceding cognitive and motor deficits. Immunohistochemistry was performed for qualitative purposes, confirming the presence of β-amyloid in the CNS of transgenic animals. Up to the third month, labeling was moderate and restricted to neuronal cell bodies; from the fifth month onward, evident extracellular deposits emerged. Behavioral assessment indicated impairments in spatial and episodic memory, as well as altered locomotor patterns in AD mice. These findings reinforce that oligoelement variations may be associated with neurodegenerative processes, including oxidative stress and synaptic dysfunction. Thus, oligoelement analysis emerges as a promising approach for the early diagnosis of AD and the monitoring of disease progression, potentially contributing to the development of new therapeutic strategies. Full article

The domain of human locomotion identification through smartphone sensors is witnessing rapid expansion within the realm of research. This domain boasts significant potential across various sectors, including healthcare, sports, security systems, home automation, and real-time location tracking. Despite the considerable volume of existing research, the greater portion of it has primarily concentrated on locomotion activities. Comparatively less emphasis has been placed on the recognition of human localization patterns. In the current study, we introduce a system by facilitating the recognition of both human physical and location-based patterns. This system utilizes the capabilities of smartphone sensors to achieve its objectives. Our goal is to develop a system that can accurately identify different human physical and localization activities, such as walking, running, jumping, indoor, and outdoor activities. To achieve this, we perform preprocessing on the raw sensor data using a Butterworth filter for inertial sensors and a Median Filter for Global Positioning System (GPS) and then applying Hamming windowing techniques to segment the filtered data. We then extract features from the raw inertial and GPS sensors and select relevant features using the variance threshold feature selection method. The extrasensory dataset exhibits an imbalanced number of samples for certain activities. To address this issue, the permutation-based data augmentation technique is employed. The augmented features are optimized using the Yeo–Johnson power transformation algorithm before being sent to a multi-layer perceptron for classification. We evaluate our system using the K-fold cross-validation technique. The datasets used in this study are the Extrasensory and Sussex Huawei Locomotion (SHL), which contain both physical and localization activities. Our experiments demonstrate that our system achieves high accuracy with 96% and 94% over Extrasensory and SHL in physical activities and 94% and 91% over Extrasensory and SHL in the location-based activities, outperforming previous state-of-the-art methods in recognizing both types of activities. Full article

The analysis of body motion is a valuable tool in the assessment and diagnosis of gait impairments, particularly those related to neurological disorders. In this study, we propose a novel automated system leveraging artificial intelligence for efficiently analyzing gait impairment from video-recorded images. The proposed methodology encompasses three key aspects. First, we generate a novel one-dimensional representation of each silhouette image, termed a silhouette sinogram, by computing the distance and angle between the centroid and each detected boundary points. This process enables us to effectively utilize relative variations in motion at different angles to detect gait patterns. Second, a one-dimensional convolutional neural network (1D CNN) model is developed and trained by incorporating the consecutive silhouette sinogram signals of silhouette frames to capture spatiotemporal information via assisted knowledge learning. This process allows the network to capture a broader context and temporal dependencies within the gait cycle, enabling a more accurate diagnosis of gait abnormalities. This study conducts training and an evaluation utilizing the publicly accessible INIT GAIT database. Finally, two evaluation schemes are employed: one leveraging individual silhouette frames and the other operating at the subject level, utilizing a majority voting technique. The outcomes of the proposed method showed superior enhancements in gait impairment recognition, with overall F1-scores of 100%, 90.62%, and 77.32% when evaluated based on sinogram signals, and 100%, 100%, and 83.33% when evaluated based on the subject level, for cases involving two, four, and six gait abnormalities, respectively. In conclusion, by comparing the observed locomotor function to a conventional gait pattern often seen in healthy individuals, the recommended approach allows for a quantitative and non-invasive evaluation of locomotion. Full article

Lameness is a crucial welfare issue in the modern dairy cattle industry, that if not identified and treated early causes losses in milk production and leads to early culling of animals. At present, the most common methods used for lameness detection and assessment are various visual locomotion scoring systems, which are labour-intensive, and the results may be subjective. The purpose of this project is to develop an integrated system for early detection of lameness in cattle, using force plate gait analysis and pattern recognition techniques to identify changes in gait which indicate the onset of lameness. The system will be tested on the natural onset of lameness in an organised farm environment. Full article

Tribulus terrestris L. (Tt) has been recently gaining attention for its pharmacological value, including its neuroprotective activities. In this study, we explore the neuroprotective effects of a Tribulus terrestris extract in a zebrafish (Danio rerio) model of scopolamine (SCOP)-induced memory impairment and brain oxidative stress. SCOP, an anticholinergic drug, was employed to replicate fundamental aspects of Alzheimer’s disease (AD) in animal models. The fish were treated with ethanolic leaf extract (ELE) from Tt (1, 3, and 6 mg/L) for 15 days. SCOP (100 µM) was administered 30 min before behavioral tests were conducted. Molecular interactions of the major compounds identified via UPLC-PDA/MS in Tt fractions with the active site of acetylcholinesterase (AChE) were explored via molecular docking analyses. Terrestrosin C, protodioscin, rutin, and saponin C exhibited the most stable binding. The spatial memory performance was assessed using the Y-maze test, and memory recognition was examined using a novel object recognition (NOR) test. Tt extract treatment reversed the altered locomotion patterns that were caused by SCOP administration. Biochemical analyses also verified Tt’s role in inhibiting AChE, improving antioxidant enzyme activities, and reducing oxidative stress markers. The present findings pave the way for future application of Tt as a natural alternative to treat cognitive disorders. Full article

This paper addresses the problem of feature encoding for gait analysis using multimodal time series sensory data. In recent years, the dramatic increase in the use of numerous sensors, e.g., inertial measurement unit (IMU), in our daily wearable devices has gained the interest of the research community to collect kinematic and kinetic data to analyze the gait. The most crucial step for gait analysis is to find the set of appropriate features from continuous time series data to accurately represent human locomotion. This paper presents a systematic assessment of numerous feature extraction techniques. In particular, three different feature encoding techniques are presented to encode multimodal time series sensory data. In the first technique, we utilized eighteen different handcrafted features which are extracted directly from the raw sensory data. The second technique follows the Bag-of-Visual-Words model; the raw sensory data are encoded using a pre-computed codebook and a locality-constrained linear encoding (LLC)-based feature encoding technique. We evaluated two different machine learning algorithms to assess the effectiveness of the proposed features in the encoding of raw sensory data. In the third feature encoding technique, we proposed two end-to-end deep learning models to automatically extract the features from raw sensory data. A thorough experimental evaluation is conducted on four large sensory datasets and their outcomes are compared. A comparison of the recognition results with current state-of-the-art methods demonstrates the computational efficiency and high efficacy of the proposed feature encoding method. The robustness of the proposed feature encoding technique is also evaluated to recognize human daily activities. Additionally, this paper also presents a new dataset consisting of the gait patterns of 42 individuals, gathered using IMU sensors. Full article

Gait scoring is a useful measure for evaluating broiler production efficiency, welfare status, bone quality, and physiology. The research objective was to track and characterize spatiotemporal and three-dimensional locomotive behaviors of individual broilers with known gait scores by jointly using deep-learning algorithms, depth sensing, and image processing. Ross 708 broilers were placed on a platform specifically designed for gait scoring and manually categorized into one of three numerical scores. Normal and depth cameras were installed on the ceiling to capture top-view videos and images. Four birds from each of the three gait-score categories were randomly selected out of 70 total birds scored for video analysis. Bird moving trajectories and 16 locomotive-behavior metrics were extracted and analyzed via the developed deep-learning models. The trained model gained 100% accuracy and 3.62 ± 2.71 mm root-mean-square error for tracking and estimating a key point on the broiler back, indicating precise recognition performance. Broilers with lower gait scores (less difficulty walking) exhibited more obvious lateral body oscillation patterns, moved significantly or numerically faster, and covered more distance in each movement event than those with higher gait scores. In conclusion, the proposed method had acceptable performance for tracking broilers and was found to be a useful tool for characterizing individual broiler gait scores by differentiating between selected spatiotemporal and three-dimensional locomotive behaviors. Full article

This study aimed to develop a vision-based gait recognition system for person identification. Gait is the soft biometric trait recognizable from low-resolution surveillance videos, where the face and other hard biometrics are not even extractable. The gait is a cycle pattern of human body locomotion that consists of two sequential phases: swing and stance. The gait features of the complete gait cycle, referred to as gait signature, can be used for person identification. The proposed work utilizes gait dynamics for gait feature extraction. For this purpose, the spatio temporal power spectral gait features are utilized for gait dynamics captured through sub-pixel motion estimation, and they are less affected by the subject’s appearance. The spatio temporal power spectral gait features are utilized for a quadratic support vector machine classifier for gait recognition aiming for person identification. Spatio temporal power spectral preserves the spatiotemporal gait features and is adaptable for a quadratic support vector machine classifier-based gait recognition across different views and appearances. We have evaluated the gait features and support vector machine classifier-based gait recognition on a locally collected gait dataset that captures the effect of view variance in high scene depth videos. The proposed gait recognition technique achieves significant accuracy across all appearances and views. Full article

Animal bodies in general and faces in particular show mirror symmetry with respect to the median-sagittal plane, with exceptions rarely occurring. Bilateral symmetry to the median sagittal plane of the body also evolved very early. From an evolutionary point of view, it should therefore have fundamental advantages, e.g., more effective locomotion and chewing abilities. On the other hand, the recognition of bilaterally symmetric patterns is an important module in our visual perception. In particular, the recognition of faces with different spatial orientations and their identification is strongly related to the recognition of bilateral symmetry. Maxillofacial surgery and Dentistry affect effective masticatory function and perceived symmetry of the lower third of the face. Both disciplines have the ability to eliminate or mitigate asymmetries with respect to form and function. In our review, we will demonstrate symmetric structures from single teeth to the whole face. We will further describe different approaches to quantify cranial, facial and dental asymmetries by using either landmarks or 3D surface models. Severe facial asymmetries are usually caused by malformations such as hemifacial hyperplasia, injury or other diseases such as Noma or head and neck cancer. This could be an important sociobiological reason for a correlation between asymmetry and perceived disfigurement. The aim of our review is to show how facial symmetry and attractiveness are related and in what way dental and facial structures and the symmetry of their shape and color influence aesthetic perception. We will further demonstrate how modern technology can be used to improve symmetry in facial prostheses and maxillofacial surgery. Full article

An exoskeleton robot is a kind of wearable mechanical instrument designed according to the shape and function of the human body. The main purpose of its design and manufacture is to enhance human strength, assist human walking and to help patients recover. The walking state of the exoskeleton robot should be highly consistent with the state of the human, so the accurate locomotion pattern recognition is the premise of the flexible control of the exoskeleton robot. In this paper, a simulated annealing (SA) algorithm-based support vector machine model is proposed for the recognition of different locomotion patterns. In order to improve the overall performance of the support vector machine (SVM), the simulated annealing algorithm is adopted to obtain the optimal parameters of support vector machine. The pressure signal measured by the force sensing resistors integrated on the sole of the shoe is fused with the position and pose information measured by the inertial measurement units attached to the thigh, shank and foot, which are used as the input information of the support vector machine. The max-relevance and min-redundancy algorithm was selected for feature extraction based on the window size of 300 ms and the sampling frequency of 100 Hz. Since the signals come from different types of sensors, normalization is required to scale the input signals to the interval (0,1). In order to prevent the classifier from overfitting, five layers of cross validation are used to train the support vector machine classifier. The support vector machine model was obtained offline in MATLAB. The finite state machine is used to limit the state transition and improve the recognition accuracy. Experiments on different locomotion patterns show that the accuracy of the algorithm is 97.47% ± 1.16%. The SA-SVM method can be extended to industrial robots and rehabilitation robots. Full article

In smartphone-based pedestrian navigation systems, detailed knowledge about user activity and device placement is a key information. Landmarks such as staircases or elevators can help the system in determining the user position when located inside buildings, and navigation instructions can be adapted to the current context in order to provide more meaningful assistance. Typically, most human activity recognition (HAR) approaches distinguish between general activities such as walking, standing or sitting. In this work, we investigate more specific activities that are tailored towards the use-case of pedestrian navigation, including different kinds of stationary and locomotion behavior. We first collect a dataset of 28 combinations of device placements and activities, in total consisting of over 6 h of data from three sensors. We then use LSTM-based machine learning (ML) methods to successfully train hierarchical classifiers that can distinguish between these placements and activities. Test results show that the accuracy of device placement classification (97.2%) is on par with a state-of-the-art benchmark in this dataset while being less resource-intensive on mobile devices. Activity recognition performance highly depends on the classification task and ranges from 62.6% to 98.7%, once again performing close to the benchmark. Finally, we demonstrate in a case study how to apply the hierarchical classifiers to experimental and naturalistic datasets in order to analyze activity patterns during the course of a typical navigation session and to investigate the correlation between user activity and device placement, thereby gaining insights into real-world navigation behavior. Full article

Advances in the field of Brain-Computer Interfaces (BCIs) aim, among other applications, to improve the movement capacities of people suffering from the loss of motor skills. The main challenge in this area is to achieve real-time and accurate bio-signal processing for pattern recognition, especially in Motor Imagery (MI). The significant interaction between brain signals and controllable machines requires instantaneous brain data decoding. In this study, an embedded BCI system based on fist MI signals is developed. It uses an Emotiv EPOC+ Brainwear^®, an Altera SoCKit^® development board, and a hexapod robot for testing locomotion imagery commands. The system is tested to detect the imagined movements of closing and opening the left and right hand to control the robot locomotion. Electroencephalogram (EEG) signals associated with the motion tasks are sensed on the human sensorimotor cortex. Next, the SoCKit processes the data to identify the commands allowing the controlled robot locomotion. The classification of MI-EEG signals from the F3, F4, FC5, and FC6 sensors is performed using a hybrid architecture of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. This method takes advantage of the deep learning recognition model to develop a real-time embedded BCI system, where signal processing must be seamless and precise. The proposed method is evaluated using k-fold cross-validation on both created and public Scientific-Data datasets. Our dataset is comprised of 2400 trials obtained from four test subjects, lasting three seconds of closing and opening fist movement imagination. The recognition tasks reach 84.69% and 79.2% accuracy using our data and a state-of-the-art dataset, respectively. Numerical results support that the motor imagery EEG signals can be successfully applied in BCI systems to control mobile robots and related applications such as intelligent vehicles. Full article

Smartphone-sensors-based human activity recognition is attracting increasing interest due to the popularization of smartphones. It is a difficult long-range temporal recognition problem, especially with large intraclass distances such as carrying smartphones at different locations and small interclass distances such as taking a train or subway. To address this problem, we propose a new framework of combining short-term spatial/frequency feature extraction and a long-term independently recurrent neural network (IndRNN) for activity recognition. Considering the periodic characteristics of the sensor data, short-term temporal features are first extracted in the spatial and frequency domains. Then, the IndRNN, which can capture long-term patterns, is used to further obtain the long-term features for classification. Given the large differences when the smartphone is carried at different locations, a group-based location recognition is first developed to pinpoint the location of the smartphone. The Sussex-Huawei Locomotion (SHL) dataset from the SHL Challenge is used for evaluation. An earlier version of the proposed method won the second place award in the SHL Challenge 2020 (first place if not considering the multiple models fusion approach). The proposed method is further improved in this paper and achieves 80.72% accuracy, better than the existing methods using a single model. Full article