Dysphagia is a prevalent symptom in numerous neurological disorders among older adults. Current dysphagia diagnostic systems either involve invasive procedures or necessitate the ingestion of liquids. Some researchers have devised automatic dysphagia detection methods based on vowels that are easy to collect and sensitive to vocal cord states. These methods extract features from each vowel separately and fuse them to train models. Nonetheless, they neglect potential interrelations among different vowels. Vowels collected from the same speaker could share subspaces since they are produced from the same vocal system. In this study, we introduce a tensor-based method that can simultaneously extract interactive information from all vowels across different modes. This method designs multi-band speech tensors and core-pruned tensor networks to investigate crucial frequency bands and connections for dysphagia screening. Experimental results show our model exceeds previous methods by approximately 10 percentage points in the classification accuracy.

Epilepsy is a common neurological disease that seriously affects the patient’s life quality. Electroencephalogram (EEG) is an important modality for epilepsy diagnosis and treatment. Here, we propose a tensor-based method using EEG, which can detect patients with seizures and identify them. In this method, we transform EEG signals into high-order tensors and design a regularized O-minus tensor network to calculate representative signals of channels. The interactive information among all tensor modes can be extracted by the regularized O-minus. Finally, the brain network is calculated based on the extracted representative signals and used as a shared feature set for epilepsy detection and patient identity. Leave one subject out cross-validation is used in seizure detection to verify the generalization of the model for first-time patients. The accuracies of seizure detection and patient identification are 88.14% and 98.59%, respectively. To our knowledge, this is the first time that epilepsy detection and patient identification are implemented within a unified framework, which could provide timely and personalized treatment for patients.

Home exercise and self-served gyms allow a larger population to exercise regularly without the cost of hiring private coaches. In absence of professional guidance, however, exercisers can suffer from injuries to muscles and joints. High-precision, affordable arm tracking with commercial, off-the-shelf (COTS) wearable devices has become an urgent need to prevent workout injuries and improve exercise performance. Recent studies with inertial measurement units (IMUs) or audio signals are neither computationally feasible for real-time motion tracking with satisfactory accuracy using COTS devices nor practically usable due to the interference with noisy ambient environments. In this paper, we propose UltraMotion, a real-time, high-precision ultrasonic arm motion tracking system designed for practical use. UltraMotion performs point cloud queries based on hidden Markov models (HMMs), a novel ultrasonic acoustic ranging method, and an extended Kalman filter (EKF) to predict the locations of all three arm joints, making it the first system offering shoulder locations. Experimental results with only a smartphone and a smartwatch demonstrate the effectiveness of UltraMotion in tracking shoulder, elbow, and wrist locations with impressively small median errors of 6.4 cm, 7.1 cm, and 8.5 cm in real-world environments, outperforming all previous systems, making UltraMotion an ideal choice for daily exercise.

Recently, the neural network has become a popular technology for pedestrian inertial navigation to avoid the errors caused by the integral part of traditional inertial navigation system and also performs better than Pedestrian Dead Reckoning(PDR). However, researchers who leverage the neural network all discard the magnetic information due to the instability of magnetic field. On account of low-cost inertial measurement unit(IMU), relying on the gyroscope and the accelerometer only will inevitably produce horizontal angular deviation. This small angular deviation will be magnified as the trajectory length increases, and finally, cause positioning drift. Through many experiments and analyses, we discovered that there is a correlation between the magnetic information and the pedestrian’s body direction under a stable magnetic field. Based on the discovery, MAGINS, a neural network inertial navigation system corrected by magnetic information was designed. A data set of motion information including IMU data and real positions was created and utilized to train a network model as the basis of our system. For the sake of the stable and available magnetic information, we designed an algorithm to quantitatively detect the stability of the magnetic field. The heading of navigation can be corrected according to the stability and the correlation mentioned above. The experiment result shows that MAGINS can detect the stability of the magnetic field precisely, and correct the heading properly since the magnetic information will not produce accumulated errors. The positioning effect of MAGINS is better than other pedestrian inertial navigation systems only based on neural network.