SUMMARYIn this paper, a new biomimetic tendon-driven actuation system for prosthetic and wearable robotic hand applications is presented. It is based on the combination of compliant tendon cables and one-way shape memory alloy (SMA) wires that form a set of agonist–antagonist artificial muscle pairs for the required flexion/extension or abduction/adduction of the finger joints. The performance of the proposed actuation system is demonstrated using a 4 degree-of-freedom (three active and one passive) artificial finger testbed, also developed based on a biomimetic design approach. A microcontroller-based pulse-width-modulated proportional-derivation (PWM-PD) feedback controller and a minimum jerk trajectory feedforward controller are implemented and tested in anad hocfashion to evaluate the performance of the finger system in emulating natural joint motions. Part II describes the dynamic modeling of the above nonlinear system, and the model-based controller design.

There is a growing interest in the use of Inertial Measurement Unit (IMU)-based systems that employ gyroscopes for gait analysis. We describe an improved IMU-based gait analysis processing method that uses gyroscope angular rate reversal to identify the start of each gait cycle during walking. In validation tests with six subjects with Parkinson disease (PD), including those with severe shuffling gait patterns, and seven controls, the probability of True-Positive event detection and False-Positive event detection was 100% and 0%, respectively. Stride time validation tests using high-speed cameras yielded a standard deviation of 6.6 ms for controls and 11.8 ms for those with PD. These data demonstrate that the use of our angular rate reversal algorithm leads to improvements over previous gyroscope-based gait analysis systems. Highly accurate and reliable stride time measurements enabled us to detect subtle changes in stride time variability following a Parkinson's exercise class. We found unacceptable measurement accuracy for stride length when using the Aminian et al gyro-based biomechanical algorithm, with errors as high as 30% in PD subjects. An alternative method, using synchronized infrared timing gates to measure velocity, combined with accurate mean stride time from our angular rate reversal algorithm, more accurately calculates mean stride length.

This paper presents the design of a novel, portable device for hand rehabilitation. The device provides for CPM (continuous passive motion) and CAM (continuous active motion) hand rehabilitation for patients recovering from damage such as flexor tendon repair and strokes. The device is capable of flexing/extending the MCP (metacarpophalangeal) and PIP (proximal interphalangeal) joints through a range of motion of 0 degrees to 90 degrees for both the joints independently. In this way, typical hand rehabilitation motions such as intrinsic plus, intrinsic minus, and a fist can be achieved without the need of any splints or attachments. The CPM mode is broken into two subgroups. The first mode is the use of preset waypoints for the device to cycle through. The second mode involves motion from a starting position to a final position, but senses the torque from the user during the cycle. Therefore the user can control the ROM by resisting when they are at the end of the desired motion. During the CPM modes the device utilizes a minimum jerk trajectory model under PD control, moving smoothly and accurately between preselected positions. CAM is the final mode where the device will actively resist the movement of the user. The user moves from a start to end position while the device produces a torque to resist the motion. This active resistance motion is a unique ability designed to mimic the benefits of a human therapist. Another unique feature of the device is its ability to independently act on both the MCP and PIP joints. The feedback sensing built into the device makes it capable of offering a wide and flexible range of rehabilitation programs for the hand.

Collaborative robots are becoming increasingly important for advanced manufacturing processes. The purpose of this paper is to determine the capability of a novel Human-Robot-interface to be used for machine hole drilling. Using a developed voice activation system, environmental factors on speech recognition accuracy are considered. The research investigates the accuracy of a Mel Frequency Cepstral Coefficients-based feature extraction algorithm which uses Dynamic Time Warping to compare an utterance to a limited, user-dependent dictionary. The developed Speech Recognition method allows for Human-Robot-Interaction using a novel integration method between the voice recognition and robot. The system can be utilised in many manufacturing environments where robot motions can be coupled to voice inputs rather than using time consuming physical interfaces. However, there are limitations to uptake in industries where the volume of background machine noise is high.

This paper aims to identify the capability of a highly flexible industrial robot modified with a high-speed machine spindle for drilling of aluminum 6061-T6. With a focus on drilling feed rate, spindle speed, and pecking cycle, the hole surface roughness and exit burr heights were investigated using the Taguchi design methodology. A state of the art condition monitoring system was used to identify the vibrations experienced during drilling operation and to establish which robot pose had increased stiffness, and thus the optimum workspace for drilling. When benchmarked against a CNC machine the results show that the CNC was capable of producing the best surface finish and the lowest burr heights. However, the robot system matched and outperformed the CNC in several experiments and there is much scope for further optimization of the process. By identifying the optimum pose for drilling together with the idealized settings, the proposed drilling system is shown to be far more flexible than a CNC milling machine and when considering the optimized drilling of aerospace aluminum this robotic solution has the potential to drastically improve productivity.