The neuromusculoskeletal modeling and simulator (NMMS) have been widely utilized in various fields and applications. The deep reinforcement learning (DRL) algorithm is a promising method to study human motor controls and movement biomechanics via NMMS without experimental data. However, existing research lacks exploration of the DRL implementation for controlling neuromusculoskeletal simulators, and only a few have presented myoelectric control systems applied to the DRL-based NMMS. In this work, an off-policy DRL algorithm, Deep Deterministic Policy Gradient (DDPG), was implemented on an upper limb NMMS with two different types of action space - direct muscle activation output and PD-based internal model, and compared their control performance. In addition, we evaluated the performance of proportional myoelectric control systems implemented on the DRL-based upper limb NMMS. The results indicate that the DRL-based NMMS can execute upper limb movements accurately, and the proportional myoelectric control system reduced the muscle activation under both types of action space. Moreover, the PD-based internal model action space shows better learning and error-tracking performance than the direct muscle activation output action space.

This paper describes the design and control architectures for a novel active thoracolumbosacral orthosis targeted at correction of abnormal postures and treatment of the human spine, often seen in adolescent idiopathic or neuromuscular scoliosis. Our novel device is motivated by the current limitations of the rigid braces used for this purpose which do not adapt to changes in the skeletal system in response to treatment. In addition, the dynamic brace can open possibilities for new treatment methods which currently do not exist. Previous brace designs were not capable of providing dynamic controlled forces. Our design utilizes two Stewart-Gough platforms in series, each controlled independently, either in position or force modes. The design can provide controlled forces/torques on different regions of spine to modify the posture. Additionally, it can control the motion of different regions of the spine through independent position control of each platform using six parallel actuators. Both control methods were validated in benchtop tests. A range of motion study was also performed with a healthy subject wearing the device while the system was controlled in transparent mode.

This paper presents computational and experimental analyses on flight dynamics of Flapping Micro Air Vehicles (FWMAV) employing a novel flapping mechanism, denoted as AIFM (Asymmetric In-phase Flapping Mechanism). This mechanism was designed to achieve controlled, asymmetric in-phase wing flapping inspired by nature's flyers. This paper extends our previous study where modeling and optimization of such a mechanism was carried out. The dynamic effects of asymmetric in-phase flapping motion during forward flight are our main focus in this study. Kinematics and rigid body dynamics modeling are first carried out to derive the equations of motion of the system, followed by aerodynamic modeling of wing using blade element theory and quasi-unsteady analysis techniques. The analysis deals with three configurations of the system: symmetric in-phase flapping motion (C0) versus asymmetric in-phase flapping motion (C+5, C-5). Scaled model of AIFM was fabricated and implemented into a FWMAV to show its practical feasibility. Some of the key dynamic effects of AIFM are addressed, suggesting AIFM solely can generate aerodynamic forces and moments which have the potential to bring higher agility and controllability to existing FWMAV platforms.

The thorax of an insect has direct flight muscles that can independently control the flapping amplitude, relative phase, and mean position of its left and right wings. This feature allows insects to modulate lateral dynamics during hovering flight, resulting in high flight maneuverability. This paper introduces the development and characterization of a novel flapping mechanism for MAVs, denoted as AIFM (Asymmetric In-phase Flapping Mechanism), that is capable of achieving controlled, asymmetric in-phase wing flapping as inspired by similar features in insects. The system consists of two 4-bar mechanisms that create basic flapping motions and two RRPR mechanisms that control the asymmetric flapping motion. The kinematics of the mechanism was investigated and optimized in such a way that enables the mechanism to produce reliable, in-phase wing motion during asymmetric flapping flight. The kinematics of the wings was evaluated both computationally and experimentally. It was shown that asymmetric wing flapping can be successfully achieved without affecting the in-phase flapping motion.

An updated image sensor to be used in a miniature microscope system for recording brain activity over a wide cortical area (4–9 mm2) is presented. The image sensor is a 100 × 100 pixel array fabricated in bulk CMOS process. The sensor also contains a 10-bit ADC and two input channels to measure other physiological signals concurrently with the optical data. Another feature of the image sensor is the ability to observe dF over sequential frames in hardware, in order to reduce the amount of data that must be read out of the image sensor at high speeds. The speed and noise performance of the image sensor is suitable for voltage-sensitive optical probes in small systems for awake and freely moving animals.

An image sensor to be used in a miniature microscope system for recording brain activity over a wide cortical area (4-9 mm2) is presented. The image sensor is a 32 times 32 pixel array fabricated in bulk CMOS process. Each pixel has a 74 mum times 34 mum photodiode. The SNR performance of the image sensor is comparable to current state-of-the-art voltage-sensitive dye imaging sensors. The power consumption and weight of the proposed microscope is much lower than other voltage-sensitive dye imaging systems and can be used on awake and freely moving animals.

We present a back-illuminated 32 × 32 pixel image sensor in 0.5-μm silicon-on-sapphire process. The imager performs “snap-shot” image acquisition and analog readout at a continuous rate of thousands of frames/s and consumes as little as 250 μW. Each pixel consists of a phototransistor and a memory capacitor in 40 μm × 40 μm with a fill factor of 43%. The image sensor is suited for hyper-spectral imaging at high speeds.

We present a back-illuminated 32 x 32 pixel SOI image sensor chip in 0.5-mum silicon-on-sapphire process capable of ultraviolet imaging. The imager performs "snap-shot" image acquisition and analog readout at a continuous rate of a thousand frames/s and consumes as little as 650 muW. Each pixel consists of a photodiode and a memory capacitor in 40 mum x 40 mum with a fill factor of 43%. The image sensor is suited for hyper-spectral imaging at high speeds.

The paper explores an algorithm and methodology for realtime video compression and communication over sensor network. Video is encoded using the address-event representation (AER) and using custom image sensors capable of detecting intensity-differences (motion) information. The work focuses on keeping a constant low bit-rate over sensor network channels based on ZigBee radios, so that the motion information in an image can degrade gracefully when the resolution or the image content is varied over time. The paper shows that a 320times240 video stream can be compressed up to 50 times while being intelligible for interpreting motion patterns. This compression requires zero computation when the image is encoded with AER. The work will be applied to remote monitoring of home-care patients and surveillance

Although imaging is an information-rich sensing modality, the use of cameras in sensor networks is very often prohibited by factors such as power, computation cost, storage, communication bandwidth and privacy. In this paper we consider information selective and privacy-preserving address-event imagers for sensor networks. Instead of providing full images with a high degree of redundancy, our efforts in the design of these imagers specialize on selecting a handful of features from a scene and outputting these features in address-event representation. In this paper we present our initial results in modeling and evaluating address-event sensors in the context of sensor networks. Using three different platforms that we have developed, we illustrate how to model address-event cameras and how to build an emulator using these models. We also present a lightweight classification scheme to illustrate the computational advantages of address-event sensors. The paper concludes with an evaluation of the classification algorithm and a feasibility study of using COTS components to emulate address-event inside a sensor network.