Reversible rearrangement of magnetic nanoparticles in solution studied using time-resolved SAXS method.

Superparamagnetic nanoparticles have broad applications in biology and medicines. Quantitative measurements of magnetic beads in solution are essential in gaining comprehensive understanding of their dynamics and developing applications. Here, using synchrotron X-ray sources combined with well controlled magnetic fields, the results from small-angle X-ray scattering (SAXS) experiments on superparamagnetic particles in solution under the influence of external magnetic fields are reported. The particles mostly remain in monodispersed states and the linear aggregates tend to be aligned with the external magnetic field. After removing the magnetic fields, the superparamagnetic nanoparticles quickly recover to their original states indicating high reversibility of the rearrangement under the control of a magnetic field. The external magnetic field instrument composed of paired permanent magnets is integrated into the SAXS beamline at the Shanghai Synchrotron Radiation Facility providing a platform for studying time-resolved dynamics induced by magnetic fields.

Design and Feasibility Study of a Leg-exoskeleton Assistive Wheelchair Robot with Tests on Gluteus Medius Muscles.

The muscles of the lower limbs directly influence leg motion, therefore, lower limb muscle exercise is important for persons living with lower limb disabilities. This paper presents a medical assistive robot with leg exoskeletons for locomotion and leg muscle exercises. It also presents a novel pedal-cycling actuation method with a crank-rocker mechanism. The mechanism is driven by a single motor with a mechanical structure that ensures user safety. A control system is designed based on a master-slave control with sensor fusion method. Here, the intended motion of the user is detected by pedal-based force sensors and is then used in combination with joystick movements as control signals for leg-exoskeleton and wheelchair motions. Experimental data is presented and then analyzed to determine robotic motion characteristics as well as the assistance efficiency with attached electromyogram (EMG) sensors. A typical muscle EMG signal analysis shows that the exercise efficiency for EMG activated amplitudes of the gluteus medius muscles approximates a walking at speed of 3 m/s when cycling at different speeds (i.e., from 16 to 80 r/min) in a wheelchair. As such, the present wheelchair robot is a good candidate for enabling effective gluteus medius muscle exercises for persons living with gluteus medius muscle disabilities.

Recognizing Continuous Multiple Degrees of Freedom Foot Movements With Inertial Sensors.

Recognition of continuous foot motions is important in robot-assisted lower limb rehabilitation, especially in prosthesis and exoskeleton design. For instance, perceiving foot motion is essential feedback for the robot controller. However, few studies have focused on perceiving multiple-degree of freedom (DOF) foot movements. This paper proposes a novel human-machine interaction (HMI) recognition wearable system for continuous multiple-DOF ankle-foot movements. The proposed system uses solely kinematic signals from inertial measurement units and multiclass support vector machines by creating error-correcting output codes. We conducted a study with multiple participants to validate the performance of the system using two strategies, a general model and a subject-specific model. The experimental results demonstrated satisfactory performance. The subject-specific approach achieved 98.45% ± 1.17% (mean ± SD) overall accuracy within a prediction time of 10.9 ms ± 1.7 ms, and the general approach achieved 85.3% ± 7.89% overall accuracy within a prediction time of 14.1 ms ± 4.5 ms. The results prove that the proposed system can more effectively recognize multiple continuous DOF foot movements than existing strategies. It can be applied to ankle-foot rehabilitation and fills the HMI high-level control demand for multiple-DOF wearable lower-limb robotics.

A Pilot Study on Continuous Breaststroke Phase Recognition with Fast Training Based on Lower-Limb Inertial Signals.

In this study, we proposed a continuous stroke phase recognition method with lower-limb inertial signals. The aim of the method was to decrease the time needed and to relieve the burdensome manual configurations in the tasks of human underwater motion recognition. The method automatically segmented the data of a period of time into stroke cycles and three sub-phases (propulsion, glide and recovery). K-nearest neighbor algorithm (k-NN) was used as the classifier to train the segmented data and classify the new data on each sample interval. To validate the proposed recognition method, three elite swimmers were recruited. We also designed an wearable sensing system for human underwater motion sensing with inertial measurement units (IMUs). With only data of 5 stroke cycles for training, the recognizer produced accurate recognition results. The average precision across the phases and the subjects was 93.7% and the average recall was 92.6%. We also investigated the time difference of the key stroke events (stroke phase transitions) between the recognized decisions and the reference ones. The average time difference was 66.2 ms, which accounted for the 4.2% of a single stroke phase. The results of the pilot study proved the feasibility of the new method for human aquatic locomotion assistance tasks. Future efforts will be paid in this new direction for more promising results.

Forearm Motion Recognition With Noncontact Capacitive Sensing.

This study presents a noncontact capacitive sensing method for forearm motion recognition. A method is proposed to record upper limb motion information from muscle contractions without contact with human skin, compensating for the limitations of existing sEMG-based methods. The sensing front-ends are designed based on human forearm shapes, and the forearm limb shape changes caused by muscle contractions will be represented by capacitance signals. After implementation of the capacitive sensing system, experiments on healthy subjects are conducted to evaluate the effectiveness. Nine motion patterns combined with 16 motion transitions are investigated on seven participants. We also designed an automatic data labeling method based on inertial signals from the measured hand, which greatly accelerated the training procedure. With the capacitive sensing system and the designed recognition algorithm, the method produced an average recognition of over 92%. Correct decisions could be made with approximately a 347-ms delay from the relaxed state to the time point of motion initiation. The confounding factors that affect the performances are also analyzed, including the sliding window length, the motion types and the external disturbances. We found the average accuracy increased to 98.7% when five motion patterns were recognized. The results of the study proved the feasibility and revealed the problems of the noncontact capacitive sensing approach on upper-limb motion sensing and recognition. Future efforts in this direction could be worthwhile for achieving more promising outcomes.

Real-Time On-Board Recognition of Continuous Locomotion Modes for Amputees With Robotic Transtibial Prostheses.

Human intent recognition is important to the control of robotic prosthesis. In this paper, we propose a multi-level real-time on-board system to recognize continuous locomotion modes. A cascaded classification strategy is designed for the recognition of six steady locomotion modes and 10 transitions. On-board signals of the robotic prosthesis include two inertial measurement units and one load cell. Three transtibial amputees are recruited in the experiments. The prediction decision time of the real-time on-board cascaded classification system is about 3.3 ms, which is enough short compared with the sliding window increment 10 ms. It is easy to recognize the standing and ambulation in the first-level classification with a 99.86% accuracy by quadratic discriminant analysis (QDA) classifier. In the second-level classification, threshold method is adopted to divide one stride into swing and stance phases. In swing phase, five steady modes are recognized with a total accuracy of 96.40%. In stance phase, all these five steady modes are recognized with a total accuracy of 91.21%. The average recognition accuracy of the three subjects is 93.21% by QDA classifier. Besides, for transitions, the proposed system could recognize all transitions rightly. The designed system is feasible and effective to realize real-time on-board recognition of continuous locomotion modes, which is promising for the further control of the prosthesis.

Origin of the Reflectin Gene and Hierarchical Assembly of Its Protein.

Cephalopods, the group of animals including octopus, squid, and cuttlefish, have remarkable ability to instantly modulate body coloration and patterns so as to blend into surrounding environments [1, 2] or send warning signals to other animals [3]. Reflectin is expressed exclusively in cephalopods, filling the lamellae of intracellular Bragg reflectors that exhibit dynamic iridescence and structural color change [4]. Here, we trace the possible origin of the reflectin gene back to a transposon from the symbiotic bioluminescent bacterium Vibrio fischeri and report the hierarchical structural architecture of reflectin protein. Intrinsic self-assembly, and higher-order assembly tightly modulated by aromatic compounds, provide insights into the formation of multilayer reflectors in iridophores and spherical microparticles in leucophores and may form the basis of structural color change in cephalopods. Self-assembly and higher-order assembly in reflectin originated from a core repeating octapeptide (here named protopeptide), which may be from the same symbiotic bacteria. The origin of the reflectin gene and assembly features of reflectin protein are of considerable biological interest. The hierarchical structural architecture of reflectin and its domain and protopeptide not only provide insights for bioinspired photonic materials but also serve as unique "assembly tags" and feasible molecular platforms in biotechnology.