To improve the accuracy of color image completion with missing entries, we present a recovery method based on generalized higher-order scalars. We extend the traditional second-order matrix model to a more comprehensive higher-order matrix equivalent, called the “t-matrix” model, which incorporates a pixel neighborhood expansion strategy to characterize the local pixel constraints. This “t-matrix” model is then used to extend some commonly used matrix and tensor completion algorithms to their higher-order versions. We perform extensive experiments on various algorithms using simulated data and publicly available images. The results show that our generalized matrix completion model and the corresponding algorithm compare favorably with their lower-order tensor and conventional matrix counterparts.

Sensor-based human activity recognition (HAR) has received considerable attention due to its wide applications in health care. Each sensor modality has its advantages and limitations. Single sensor modalities sometimes may not cope with complex situations in practice. To resolve this challenge, we design and develop a practical hybrid sensory HAR system for older people. To enhance the performance of the system, we propose a unique data fusion method through combining both wearable sensors and ambient sensors. The wearable sensors in this paper are used for identifying the specific daily activities. The ambient sensors delivering the occupant’s room-level daily routine provide a more comprehensive surveillance with the wearable sensors together; meanwhile, the captured room-level location information is also used in the data fusion to trigger the sub classification models pretrained by wearable data. We also explore a new feature set extracted from wearable sensors to improve the system performance. We experimentally evaluate our system by applying four typical mutual information-based feature selection methods and the support vector machines classification algorithm instead of other complex algorithms, with the aim of exploring a practical way to improve recognition accuracy. The ground-truth data are gathered from 21 subjects, including 17 daily activities with the sample size of 2,142,000. The experimental results demonstrate the effectiveness of our method. The new feature set help improve the accuracy to 96.82%±0.15 from 89.81%±0.54 using wearable data only; and the data fusion with ambient information achieves a further increased accuracy of 98.32%.

To date, several alterations in the gait pattern can be treated through rehabilitative approaches and robot assisted therapy (RAT). Gait data and gait trajectories are essential in specific exoskeleton control strategies. Nevertheless, the scarcity of human gait data due to the high cost of data collection or privacy concerns can hinder the performance of controllers or models. This paper thus first creates a GANs-based (Generative Adversarial Networks) data augmentation method to generate synthetic human gait data while still retaining the dynamics of the real gait data. Then, both the real collected and the synthesized gait data are fed to our constructed two-stage attention model for gait trajectories prediction. The real human gait data are collected with the five healthy subjects recruited from an optical motion capture platform. Experimental results indicate that the created GANs-based data augmentation model can synthesize realistic-looking multi-dimensional human gait data. Also, the two-stage attention model performs better compared with the LSTM model; the attention mechanism shows a higher capacity of learning dependencies between the historical gait data to accurately predict the current values of the hip joint angles and knee joint angles in the gait trajectory. The predicted gait trajectories depending on the historical gait data can be further used for gait trajectory tracking strategies.

With advances in machine learning and ambient sensors as well as the emergence of ambient assisted living (AAL), modeling humans’ abnormal behaviour patterns has become an important assistive technology for the rising elderly population in recent decades. Abnormal behaviour observed from daily activities can be an indicator of the consequences of a disease that the resident might suffer from or of the occurrence of a hazardous incident. Therefore, tracking daily life activities and detecting abnormal behaviour are significant in managing health conditions in a smart environment. This paper provides a comprehensive and in-depth review, focusing on the techniques that profile activities of daily living (ADL) and detect abnormal behaviour for healthcare. In particular, we discuss the definitions and examples of abnormal behaviour/activity in the healthcare of elderly people. We also describe the public ground-truth datasets along with approaches applied to produce synthetic data when no real-world data are available. We identify and describe the key facets of abnormal behaviour detection in a smart environment, with a particular focus on the ambient sensor types, datasets, data representations, conventional and deep learning-based abnormal behaviour detection methods. Finally, the survey discusses the challenges and open questions, which would be beneficial for researchers in the field to address.

Conventional repetitive control (RC) with an internal model constant $Q$ less than 1 has a limited resonant bandwidth and a limited open-loop gain and then has mediocre steady-state and dynamic performance when the frequencies of reference and disturbance signals fluctuate. To solve the problem, in this article, we present a feedforward repetitive control (FRC) scheme for grid-tied inverters in microgrids to track the fundamental reference signal and suppress the harmonic signals simultaneously. The proposed FRC scheme consists of a proportional controller in parallel with a modified RC controller with a feedforward channel. Compared with conventional RC, the proposed FRC has wider resonant bandwidths and much larger open-loop gains at the frequencies of interest. Taking the feedforward odd harmonic repetitive control (FORC) as an example, the stability analysis and the detailed parameter optimization design guideline of FORC are given. Experimental results verify that the proposed FORC scheme has a better immunity to grid frequency fluctuation and a faster dynamic response when the grid frequency fluctuates.