STAR: A Concise Deep Learning Framework for Citywide Human Mobility Prediction

Human mobility forecasting in a city is of utmost importance to transportation and public safety, but with the process of urbanization and the generation of big data, intensive computing and determination of mobility pattern have become challenging. This study focuses on how to improve the accuracy and efficiency of predicting citywide human mobility via a simpler solution. A spatio-temporal mobility event prediction framework based on a single fully-convolutional residual network (STAR) is proposed. STAR is a highly simple, general and effective method for learning a single tensor representing the mobility event. Residual learning is utilized for training the deep network to derive the detailed result for scenarios of citywide prediction. Extensive benchmark evaluation results on real-world data demonstrate that STAR outperforms state-of-the-art approaches in single- and multi-step prediction while utilizing fewer parameters and achieving higher efficiency.Comment: Accepted by MDM 201

Mutual information inspired feature selection using kernel canonical correlation analysis

This paper proposes a filter-based feature selection method by combining the measurement of kernel canonical correlation analysis (KCCA) with the mutual information (MI)-based feature selection method, named mRMJR-KCCA. The mRMJR-KCCA maximizes the relevance between the feature candidate and the target class labels and simultaneously minimizes the joint redundancy between the feature candidate and the already selected features in the view of KCCA. To improve the computation efficiency, we adopt the Incomplete Cholesky Decomposition to approximate the kernel matrix in implementing the KCCA in mRMJR-KCCA for larger-size datasets. The proposed method is experimentally evaluated on 13 classification-associated datasets. Compared with certain popular feature selection methods, the experimental results demonstrate the better performance of the proposed mRMJR-KCC

A survey on wearable sensor modality centred human activity recognition in health care

Increased life expectancy coupled with declining birth rates is leading to an aging population structure. Aging-caused changes, such as physical or cognitive decline, could affect people's quality of life, result in injuries, mental health or the lack of physical activity. Sensor-based human activity recognition (HAR) is one of the most promising assistive technologies to support older people's daily life, which has enabled enormous potential in human-centred applications. Recent surveys in HAR either only focus on the deep learning approaches or one specific sensor modality. This survey aims to provide a more comprehensive introduction for newcomers and researchers to HAR. We first introduce the state-of-art sensor modalities in HAR. We look more into the techniques involved in each step of wearable sensor modality centred HAR in terms of sensors, activities, data pre-processing, feature learning and classification, including both conventional approaches and deep learning methods. In the feature learning section, we focus on both hand-crafted features and automatically learned features using deep networks. We also present the ambient-sensor-based HAR, including camera-based systems, and the systems which combine the wearable and ambient sensors. Finally, we identify the corresponding challenges in HAR to pose research problems for further improvement in HAR

A Data Fusion-Based Hybrid Sensory System for Older People’s Daily Activity and Daily Routine Recognition

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%

A novel human-carrying quadruped walking robot

This article adopts a 2-UPS+UP (U, P, and S are universal joint, the prismatic joint, and sphere joint, respectively) parallel mechanism as the leg mechanism of the quadruped walking robot based on the bionic concept and the motion capacity of the leg mechanism. The article investigates the kinematics (including the leg mechanism and the whole mechanism), gait planning, control, and experiment in detail. The following tasks are conducted: (1) designing the whole mechanism and developing the kinematics equations for both the leg mechanism and the whole mechanism; (2) planning the trotting gait and designing the foot trajectory based on the robot characteristics and conducting the kinematics analysis; (3) building the control system of the robot using self-developed controllers and drivers and studying the compound position control strategy; and (4) conducting the experiments for validating the controller, the compound position control strategy, the trotting pace, carrying capacity, and human-carrying walking. The results confirm that the proposed human-carrying walking robot has good performance and it is also verified that the controller and the compound position control strategy are suitable

Advanced Intelligent Control in Robots

[Abstract not available.

Dual-blockchain based multi-layer grouping federated learning scheme for heterogeneous data in industrial IoT

Federated Learning (FL) allows data owners to train neural networks together without sharing local data, allowing the Industrial Internet of Things (IIoT) to share a variety of data. However, traditional federated learning frameworks suffer from data heterogeneity and outdated models. To address these issues, this paper proposed a dual-blockchain based multi-layer grouping federated learning architecture (BMFL). BMFL divides the participant groups based on the training tasks, then realizes the model training combining synchronous and asynchronous through the multi-layer grouping structure, and uses the model blockchain to record the characteristic tags of the global model, allowing group-manners to extract the model based on the feature requirements and solving the problem of data heterogeneity. In addition, to protect the privacy of the model gradient parameters and manage the key, the global model is stored in ciphertext, and the chameleon hash algorithm is used to perform the modification and management of the encrypted key on the key blockchain while keeping the block header hash unchanged. Finally, we evaluate the performance of BMFL on different public datasets and verify the practicality of the scheme with real fault dataset. The experimental results show that the proposed BMFL exhibits more stable and accurate convergence behavior than the classic FL algorithm, and the key revocation overhead time is reasonable

Mechanical design and trajectory planning of a lower limb rehabilitation robot with a variable workspace

The early phase of extremity rehabilitation training has high potential impact for stroke patients. However, most of the lower limb rehabilitation robots in hospitals are proposed just suitable for patients at the middle or later recovery stage. This article investigates a new sitting/lying multi-joint lower limb rehabilitation robot. It can be used at all recovery stages, including the initial stage. Based on man–machine engineering and the innovative design for mechanism, the leg length of the lower limb rehabilitation robot is automatically adjusted to fit patients with different heights. The lower limb rehabilitation robot is a typical human–machine system, and the limb safety of the patient is the most important principle to be considered in its design. The hip joint rotation ranges are different in people’s sitting and lying postures. Different training postures cannot make the training workspace unique. Besides the leg lengths and joint rotation angles varied with different patients, the idea of variable workspace of the lower limb rehabilitation robot is first proposed. Based on the variable workspace, three trajectory planning methods are developed. In order to verify the trajectory planning methods, an experimental study has been conducted. Theoretical and actual curves of the hip rotation, knee rotation, and leg mechanism end point motion trajectories are obtained for three unimpaired subjects. Most importantly, a clinical trial demonstrated the safety and feasibility of the proposed lower limb rehabilitation robot

Data-based bipartite formation control for multi-agent systems with communication constraints

This article investigates data-driven distributed bipartite formation issues for discrete-time multi-agent systems with communication constraints. We propose a quantized data-driven distributed bipartite formation control approach based on the plant’s quantized and saturated information. Moreover, compared with existing results, we consider both the fixed and switching topologies of multi-agent systems with the cooperative-competitive interactions. We establish a time-varying linear data model for each agent by utilizing the dynamic linearization method. Then, using the incomplete input and output data of each agent and its neighbors, we construct the proposed quantized data-driven distributed bipartite formation control scheme without employing any dynamics information of multi-agent systems. We strictly prove the convergence of the proposed algorithm, where the proposed approach can ensure that the bipartite formation tracking errors converge to the origin, even though the communication topology of multi-agent systems is time-varying switching. Finally, simulation and hardware tests demonstrate the effectiveness of the proposed scheme