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Objective: Physical and cognitive workloads and performance were studied for a corrective shared control (CSC) human–robot collaborative (HRC) sanding task. Background: Manual sanding is physically demanding. Collaborative robots (cobots) can potentially reduce physical stress, but fully autonomous implementation has been particularly challenging due to skill, task variability, and robot limitations. CSC is an HRC method where the robot operates semi-autonomously while the human provides real-time corrections. Methods: Twenty laboratory participants removed paint using an orbital sander, both manually and with a CSC robot. A fully automated robot was also tested. Results: The CSC robot improved subjective discomfort compared to manual sanding in the upper arm by 29.5%, lower arm by 32%, hand by 36.5%, front of the shoulder by 24%, and back of the shoulder by 17.5%. Muscle fatigue measured using EMG, was observed in the medial deltoid and flexor carpi radialis for the manual condition. The composite cognitive workload on the NASA-TLX increased by 14.3% for manual sanding due to high physical demand and effort, while mental demand was 14% greater for the CSC robot. Digital imaging showed that the CSC robot outperformed the automated condition by 7.16% for uniformity, 4.96% for quantity, and 6.06% in total. Conclusions: In this example, we found that human skills and techniques were integral to sanding and can be successfully incorporated into HRC systems. Humans performed the task using the CSC robot with less fatigue and discomfort. Applications: The results can influence implementation of future HRC systems in manufacturing environments. [ABSTRACT FROM AUTHOR]

Observer, manual single-frame video, and automated computer vision measures of the Hand Activity Level (HAL) were compared. HAL can be measured three ways: (1) observer rating (HALO), (2) calculated from single-frame multimedia video task analysis for measuring frequency (F) and duty cycle (D) (HALF), or (3) from automated computer vision (HALC). This study analysed videos collected from three prospective cohort studies to ascertain HALO, HALF, and HALC for 419 industrial videos. Although the differences for the three methods were relatively small on average (<1), they were statistically significant (p <.001). A difference between the HALC and HALF ratings within ±1 point on the HAL scale was the most consistent, where more than two thirds (68%) of all the cases were within that range and had a linear regression through the mean coefficient of 1.03 (R2 = 0.89). The results suggest that the computer vision methodology yields comparable results as single-frame video analysis. Practitioner summary: The ACGIH Hand Activity Level (HAL) was obtained for 419 industrial tasks using three methods: observation, calculated using single-frame video analysis and computer vision. The computer vision methodology produced results that were comparable to single-frame video analysis. [ABSTRACT FROM AUTHOR]

In this work, a distributed multi-platform active debris removal mission planning task is formulated as a distributed constrained optimization problem (DCOP), and a novel Synchronous Incomplete Searching Algorithm under Chain Topology (SISACT) is proposed. We envision a distributive cooperative mission planning scenario where multiple debris removal platforms generate individual mission sub-plans on how to remove a subset of assigned debris subject to constraints on mission duration and orbit transfer cost. The overall goal is to maximize the aggregate reward for debris removal while avoiding conflicts among sub-plans. To resolve the potential conflicts, multiple platforms must communicate each other intensively. The proposed SISACT algorithm can reduce the inter-platform communication burdens by inducing a fully connected constraint graph into a chain graph with domain assignment. SISACT is designed to focus on global collaboration among the platforms for mission plan partitioning while leveraging existing single platform mission planning algorithms to provide details of the sub-plans. Simulation with a realistic active debris removal scenario has been carried out. It is shown that the proposed SISACT algorithm can optimize the overall reward of the mission while satisfying all the constraints. This validates the effectiveness of SISACT. • A new multi-platform ADR mission planning is proposed based on DCOP. • A new DCOP model with fully-connected all-hard constraint graph is presented. • Proposed method can satisfy all constraints without changing solution frequently. • Tests on Iridium 33 debris set with 4 platforms verify its efficiency. [ABSTRACT FROM AUTHOR]

A novel wearable multi-sensor data glove system is developed to explore the relation between finger spasticity and voluntary movement in patients with stroke. Many stroke patients suffer from finger spasticity, which is detrimental to their manual dexterity. Diagnosing and assessing the degrees of spasticity require neurological testing performed by trained professionals to estimate finger spasticity scores via the modified Ashworth scale (MAS). The proposed system offers an objective, quantitative solution to assess the finger spasticity of patients with stroke and complements the manual neurological test. In this work, the hardware and software components of this system are described. By requiring patients to perform five designated tasks, biomechanical measurements including linear and angular speed, acceleration, and pressure at every finger joint and upper limb are recorded, making up more than 1000 features for each task. We conducted a preliminary clinical test with 14 subjects using this system. Statistical analysis is performed on the acquired measurements to identify a small subset of features that are most likely to discriminate a healthy patient from patients suffering from finger spasticity. This encouraging result validates the feasibility of this proposed system to quantitatively and objectively assess finger spasticity. [ABSTRACT FROM AUTHOR]

A novel power-line interference (PLI) detection and suppression algorithm is presented to preprocess the electrocardiogram (ECG) signals. A distinct feature of this proposed algorithm is its ability to detect the presence of PLI in the ECG signal before applying the PLI suppression algorithm. No PLI suppression operation will be performed if PLI is not detected. We propose a PLI detector that employs an optimal linear discriminant analysis (LDA) algorithm to make a decision for the PLI presence. An efficient recursive least-squares (RLS) adaptive notch filter is also developed to serve the purpose of PLI suppression. Experimental results demonstrate superior performance of this proposed algorithm. Index Terms--Electrocardiogram (ECG), linear discriminant analysis (LDA), power-fine interference (PLI), recursive least squares (RLS).

An efficient implementation of discrete cosine transform (DCT) computations are presented based on the so-called shifted discrete Fourier transform (SDFT), a generalization of the conventional DFT (DFT). Due to the simple form of the factorized matrices, the derived architecture can be easily constructed from the cascade of only two types of parameterized hardware modules: butterfly operators and rotators. The butterfly operator performs the conventional butterfly shuffling and addition/subtraction. The rotator that performs plane rotations of two-dimensional (2-D) vectors is designed using carry-save-adder (CSA)-based unfolded pipelined CORDIC architecture where the rotation angles can be approximated with different accuracies using a sequence of bipolar signs. The proposed one-dimensional and 2-D DCT implementations composed of the above two types of parameterized modules can be used as flexible and reusable Silicon Intellectual Property (SIP) for the DCT computation unit to be embedded in system-on-a-chip (SoC) design. The proposed implementations have many features and advantages, including SIP reusability, low complexity, high-throughput, regularity, scalability (easy extension of transform length), and flexibility (approximated DCT with various accuracies). Index Terms--Discrete cosine transform (DCT), shifted discrete Fourier transform (SDFT), integer DCT (IDCT), binDCT, CORDIC, very large-scale integration (VLSI) design.

Atmospheric lidar observations provide a unique capability to directly observe the vertical profile of cloud and aerosol scattering properties and have proven to be an important capability for the atmospheric science community. For this reason NASA and ESA have put a major emphasis on developing both space and ground based lidar instruments. Measurement noise (solar background and detector noise) has proven to be a significant limitation and is typically reduced by temporal and vertical averaging. This approach has significant limitations as it results in significant reduction in the spatial information and can introduce biases due to the non-linear relationship between the signal and the retrieved scattering properties. This paper investigates a new approach to de-noising and retrieving cloud and aerosol backscatter properties from lidar observations that leverages a technique developed for medical imaging to de-blur and de-noise images; the accuracy is defined as the error between the true and inverted photon rates. Hence non-linear bias errors can be mitigated and spatial information can be preserved.

Objective: A computer vision method was developed for estimating the trunk flexion angle, angular speed, and angular acceleration by extracting simple features from the moving image during lifting. Background: Trunk kinematics is an important risk factor for lower back pain, but is often difficult to measure by practitioners for lifting risk assessments. Methods: Mannequins representing a wide range of hand locations for different lifting postures were systematically generated using the University of Michigan 3DSSPP software. A bounding box was drawn tightly around each mannequin and regression models estimated trunk angles. The estimates were validated against human posture data for 216 lifts collected using a laboratory-grade motion capture system and synchronized video recordings. Trunk kinematics, based on bounding box dimensions drawn around the subjects in the video recordings of the lifts, were modeled for consecutive video frames. Results: The mean absolute difference between predicted and motion capture measured trunk angles was 14.7°, and there was a significant linear relationship between predicted and measured trunk angles (R2 =.80, p <.001). The training error for the kinematics model was 2.3°. Conclusion: Using simple computer vision-extracted features, the bounding box method indirectly estimated trunk angle and associated kinematics, albeit with limited precision. Application: This computer vision method may be implemented on handheld devices such as smartphones to facilitate automatic lifting risk assessments in the workplace. [ABSTRACT FROM AUTHOR]

In the reconstruction of cylindrical phase-only hologram (POH), speckle noise caused by the addition of random phase is rarely discussed. Although some speckle noise suppression algorithms have been proposed for planar POH, it does not work well if they are directly applied to cylindrical POH. To solve this issue, the cylindrical self-diffraction iteration (CSDI) algorithm is proposed. This is an iteration algorithm based on the cylindrical self-diffraction model which is a special case of the cylindrical backward-propagation model at a limit condition. Simulation results show that CSDI can suppress speckle noise and improve the quality of reconstructed images significantly. Furthermore, the proposed CSDI algorithm outperforms conventional algorithms, which work well in planar POH and are directly applied to cylindrical POH, in suppressing speckle noise for cylindrical POH. To our best knowledge, the models of cylindrical backward-propagation and cylindrical self-diffraction as well as the concept of CSDI are firstly proposed and applied to speckle suppression of cylindrical POH. [ABSTRACT FROM AUTHOR]

A novel digital adaptive blind calibration technique for frequency-interleaved analog-to-digital converters (FI-ADCs) with arbitrary numbers of channels is proposed. It performs the estimation and correction of multiple inherent circuit deficiencies alternately and recursively in the background, including spectral leakage, harmonic folding, jitter, I/Q mirror and aliasing images, and suffices to deliver projected calibration capabilities with deliberately chosen initial guesses. We are of the opinion that this is the first study to address the joint channel identification and error compensation problem encountered in the FI-ADC design. For the efficient implementation, an analytical time-interleaving-like equivalent model is formulated such that the number of unknown mismatch parameters is significantly reduced, and signals devoted to cyclic estimation are maximally decimated. This derived model not only propounds a better perspective for comprehension and interpretation of the FI system mechanism but also enables sufficient scalability and flexibility of the developed calibration framework. Extensive simulation results further show that this novel architecture achieves a good balance between the computational complexity and convergence, and provides an exceptional and relatively flat effective number of bit (ENOB) performance over a wide bandwidth. [ABSTRACT FROM AUTHOR]

The double phase method is an efficient way to generate phase-only holograms with high reconstruction quality due to no addition of a random phase. However, it cannot directly encode the Fourier spectrum because of the limited gray modulation range of spatial light modulator, and quantization error. This shortage restricts the application of the double phase in the phase-only Fourier hologram, and this issue is never discussed, and solved as far as we know. To solve this issue, we propose a method to generate phase-only Fourier hologram by analyzing quantization error, and adding a proper quadratic phase. The proposed method overcomes the shortage, and outperforms the non-iterative bidirectional error diffusion method in reconstruction quality, and calculation speed with 8.9 dB higher, and 33 times faster on average, respectively. [ABSTRACT FROM AUTHOR]

Video salient object detection aims at distinguishing the salient objects from the complex background and highlighting them uniformly in the spatiotemporal domain, which still suffers from the interference of the complicated dynamic background in unconstrained videos. To address this problem, we propose a novel coarse-to-fine spatiotemporal salient object detection method. Specifically, we first model a novel motion energy to exclude the motion noise by exploiting the motion magnitude and motion orientation. Then, a supervoxel-level inter-frame graph model is constructed for each pair of adjacent frames independently, and a robust graph clustering-based saliency seed generation method is proposed to produce a coarse saliency map. Furthermore, the supervoxel-level inter-frame graph model is reconstructed by considering the regional spatiotemporal consistency constraint based on the coarse saliency map. The prior information obtained from pixel clustering is also taken into account to optimize the weight of the inter-frame graph model. Finally, a multi-graphs saliency propagation method is exploited under the manifold regularization framework by fusing the motion energy and appearance feature to refine the coarse saliency map. The extensive experiments on two widely used datasets validate the effectiveness and superiority of the proposed method against 13 state-of-the-art methods in terms of PR-curves, scores of S-measure, $F_{\beta }$ , and MAE. [ABSTRACT FROM AUTHOR]

A cross-layer design methodology that jointly optimizes the link-layer network flow rate and the physical layer transmission power wherein rate is developed for energy-constrained underwater wireless sensor networks. The objective of the design is to maximize the lifetime of the network operation for a given network topology and a bound of the total transmission time of all links. Initially, each node is set to transmit at the most energy-efficient transmission rate, which is derived using an underwater acoustic channel model. A constrained least square problem then is formulated which yields an analytical solution of the optimal network flow at each link that maximizes the network lifetime. If the timing constraint is not met, a new iteration will begin with the restriction on per-node energy consumption incrementally relaxed. Usually, the algorithm converges within a couple of iterations. Two design examples, a linear configured network, and a rhombus configured network are used to demonstrate the design procedures. Extensive simulations have been performed and superior performance (longer network lifetime) of this proposed design has been observed. [ABSTRACT FROM AUTHOR]

Objective: This study explores how common machine learning techniques can predict surgical maneuvers from a continuous video record of surgical benchtop simulations.Background: Automatic computer vision recognition of surgical maneuvers (suturing, tying, and transition) could expedite video review and objective assessment of surgeries.Method: We recorded hand movements of 37 clinicians performing simple and running subcuticular suturing benchtop simulations, and applied three machine learning techniques (decision trees, random forests, and hidden Markov models) to classify surgical maneuvers every 2 s (60 frames) of video.Results: Random forest predictions of surgical video correctly classified 74% of all video segments into suturing, tying, and transition states for a randomly selected test set. Hidden Markov model adjustments improved the random forest predictions to 79% for simple interrupted suturing on a subset of randomly selected participants.Conclusion: Random forest predictions aided by hidden Markov modeling provided the best prediction of surgical maneuvers. Training of models across all users improved prediction accuracy by 10% compared with a random selection of participants.Application: Marker-less video hand tracking can predict surgical maneuvers from a continuous video record with similar accuracy as robot-assisted surgical platforms, and may enable more efficient video review of surgical procedures for training and coaching. [ABSTRACT FROM AUTHOR]