Dual drive series actuator

Industrial robotic manipulators can be found in most factories today. Their tasks are accomplished through actively moving, placing and assembling parts. This movement is facilitated by actuators that apply a torque in response to a command signal. The presence of friction and possibly backlash have instigated the development of sophisticated compensation and control methods in order to achieve the desired performance may that be accurate motion tracking, fast movement or in fact contact with the environment. This thesis presents a dual drive actuator design that is capable of physically linearising friction and hence eliminating the need for complex compensation algorithms. A number of mathematical models are derived that allow for the simulation of the actuator dynamics. The actuator may be constructed using geared dc motors, in which case the benefits of torque magnification is retained whilst the increased non-linear friction effects are also linearised. An additional benefit of the actuator is the high quality, low latency output position signal provided by the differencing of the two drive positions. Due to this and the linearised nature of friction, the actuator is well suited for low velocity, stop-start applications, micro-manipulation and even in hard-contact tasks. There are, however, disadvantages to its design. When idle, the device uses power whilst many other, single drive actuators do not. Also the complexity of the models mean that parameterisation is difficult. Management of start-up conditions still pose a challenge

Current over-stressing small DC motors to evaluate performance limits of electromechanical actuators for haptic applications

Actuators for haptic devices tend to have a different set of requirements in comparison to many other engineering applications. Small permanent magnet DC electric motors are commonly used as actuators in haptic devices and, in operation, tend to spend a significant period of time in a `stalled' condition where they are attempting to oppose an applied force. Ideally a haptic actuator together with its power amplifier exchange energy reversibly with the mechanical loads. However this is not feasible at room temperature and to achieve good force performance results in energy loss as heat in the motor windings. This paper identifies the relationship between heat loss and force generation in haptic electromagnetic actuators. The work then presents results on current over-stressing of small DC motors so as to understand the risks of demagnetisation against thermal damage to the armature. Results indicate that it should be possible to apply short current over-stresses to commercial DC permanent magnet motors to increase end point force. Also by paying careful attention to heat dissipation in the design of small permanent magnet actuators motors, it should be possible to improve the overall performance of actuators for haptic applications

Energy-efficient hybrid system for Wireless Body Area Network Applications

Wireless Body Area Networks (WBANs) consist of a number of miniaturized wearable or implanted sensor nodes that are employed to monitor vital parameters of a patient over long duration of time. These sensors capture physiological data and wirelessly transfer the collected data to a local base station in order to be further processed. Almost all of these body sensors are expected to have low data-rate and to run on a battery. Since recharging or replacing the battery is not a simple task specifically in the case of implanted devices such as pacemakers, extending the lifetime of sensor nodes in WBANs is one of the greatest challenges. To achieve this goal, WBAN systems employ low-power communication transceivers and low duty cycle Medium Access Control (MAC) protocols. Although, currently used MAC protocols are able to reduce the energy consumption of devices for transmission and reception, yet they are still unable to offer an ultimate energy self-sustaining solution for low-power MAC protocols. This paper proposes to utilize energy harvesting technologies in low-power MAC protocols. This novel approach can further reduce energy consumption of devices in WBAN systems

Detailed examination of a packet collision model for Bluetooth Low Energy advertising mode

The aim of this paper is to investigate the amount of energy that is required to successfully transmit information inside the Bluetooth Low Energy (BLE) advertising packets. There are applications that require more than one BLE node to simultaneously transmit data. The BLE protocol utilizes a specific communication method termed advertising mode to perform unidirectional broadcasts of data from the advertising devices. However, with an increased number of BLE devices advertising simultaneously, there will be inevitable packet collisions from the advertising devices. This results in a waste of energy, specifically in low-power applications where lower consumption is desirable to minimize the need for battery replacements. This paper examines a packet collision model for the BLE advertising mode with the results validated using experimental data. Our analysis shows that when the throughput of the BLE network starts to fall due to an increase in the number of packet collisions, the energy consumption of the BLE nodes increase exponentially with respect to the number of nodes

Analysis of recovery effect in supercapacitors for wearable devices

Supercapacitors are likely to be adopted as power sources for wearable sensors; in particular where the sensor mechanism relies on energy harvesting. A specific advantage of supercapacitors over traditional batteries is their performance over large numbers of discharge cycles. Likewise, in the case of wearable devices, it is essential to efficiently manage the available power. Supercapacitors exhibit a small recovery effect, in part due to ion diffusion. Modelling this effect allows an increase in available energy to be realized following the sleep times during a discharge cycle thus increasing the time between charging for wearables. This paper presents the increase in useful lifetime that can be achieved via the recovery effect in a typical wearable device

Reconstruction of Angular Kinematics From Wrist-Worn Inertial Sensor Data for Smart Home Healthcare

This paper tackles the problem of the estimation of simplified human limb kinematics for home health care. Angular kinematics are widely used for gait analysis, for rehabilitation, and more generally for activity recognition. Residential monitoring requires particular sensor constraints to enable long-term user compliance. The proposed strategy is based on measurements from two low-power accelerometers placed only on the forearm, which makes it an ill-posed problem. The system is considered in a Bayesian framework, with a linear-Gaussian transition model with hard boundaries and a nonlinear-Gaussian observation model. The state vector and the associated covariance are estimated by a post-regularized particle filter (constrained-extended-RPF or C-ERPF), with an importance function whose moments are computed via an extended Kalman filter (EKF) linearization. Several sensor configurations are compared in terms of estimation performance, as well as power consumption and user acceptance. The proposed constrained-EKF (CERPF) is compared to other methods (EKF, constrained-EKF, and ERPF without transition constraints) on the basis of simulations and experimental measurements with motion capture reference. The proposed C-ERPF method coupled with two accelerometers on the wrist provides promising results with 19% error in average on both angles, compared with the motion capture reference, 10% on velocities and 7% on accelerations. This comparison highlights that arm kinematics can be estimated from only two accelerometers on the wrist. Such a system is a crucial step toward enabling machine monitoring of users health and activity on a daily basis

Dictionary memory based software architecture for distributed bluetooth low energy host controllers enabling high coverage in consumer residential healthcare environments

Technology has been seen as a possible solution to the increasing costs of healthcare and the globally aging population. It is known that many elderly people prefer to stay in their homes for as long as possible and remote monitoring can be a solution, but often such systems lack useful information or are prohibitive due to cost, ease of use/deployment and wireless coverage. This work presents a novel gateway software architecture based on threads being managed by dictionary memory. The architecture has been deployed in a distributed interconnected set of low-cost consumer grade gateway devices using Bluetooth Low Energy (BLE) that are positioned around the home. The gateway devices can then be used to listen, monitor or connect to BLE based healthcare sensors to continually reveal information about the user with full residential coverage. A further novelty of this work is the ability to maintain handover connections between many sensors and many gateways as a user moves throughout their home, thus the gateways can route information to/from sensors across the consumer’s home network. The system has been tested in an experimental house and is now poised to be initially deployed to 100 homes for residential healthcare monitoring before any public mass consumer deployment

Could in-home sensors surpass human observation of people with Parkinson’s at high risk of falling? An ethnographic study

Self-report underpins our understanding of falls among people with Parkinson’s (PwP) as they largely happen unwitnessed at home. In this qualitative study, we used an ethnographic approach to investigate which in-home sensors, in which locations, could gather useful data about fall risk. Over six weeks, we observed five independently mobile PwP at high risk of falling, at home. We made field notes about falls (prior events and concerns) and recorded movement with video, Kinect, and wearable sensors. The three women and two men (aged 71 to 79 years) having moderate or severe Parkinson’s were dependent on others and highly sedentary. We most commonly noted balance protection, loss, and restoration during chair transfers, walks across open spaces and through gaps, turns, steps up and down, and tasks in standing (all evident walking between chair and stairs, e.g.). Our unobtrusive sensors were acceptable to participants: they could detect instability during everyday activity at home and potentially guide intervention. Monitoring the route between chair and stairs is likely to give information without invading the privacy of people at high risk of falling, with very limited mobility, who spend most of the day in their sitting rooms

Identifying balance impairments in people with Parkinson's disease using video and wearable sensors

Falls and near falls are common among people with Parkinson's (PwP). To date, most wearable sensor research focussed on fall detection, few studies explored if wearable sensors can detect instability. Can instability (caution or near-falls) be detected using wearable sensors in comparison to video analysis? Twenty-four people (aged 60-86) with and without Parkinson's were recruited from community groups. Movements (e.g. walking, turning, transfers and reaching) were observed in the gait laboratory and/or at home; recorded using clinical measures, video and five wearable sensors (attached on the waist, ankles and wrists). After defining 'caution' and 'instability', two researchers evaluated video data and a third the raw wearable sensor data; blinded to each other's evaluations. Agreement between video and sensor data was calculated on stability, timing, step count and strategy. Data was available for 117 performances: 82 (70%) appeared stable on video. Ratings agreed in 86/117 cases (74%). Highest agreement was noted for chair transfer, timed up and go test and 3 m walks. Video analysts noted caution (slow, contained movements, safety-enhancing postures and concentration) and/or instability (saving reactions, stopping after stumbling or veering) in 40/134 performances (30%): raw wearable sensor data identified 16/35 performances rated cautious or unstable (sensitivity 46%) and 70/82 rated stable (specificity 85%). There was a 54% chance that a performance identified from wearable sensors as cautious/unstable was so; rising to 80% for stable movements. Agreement between wearable sensor and video data suggested that wearable sensors can detect subtle instability and near-falls. Caution and instability were observed in nearly a third of performances, suggesting that simple, mildly challenging actions, with clearly defined start- and end-points, may be most amenable to monitoring during free-living at home. Using the genuine near-falls recorded, work continues to automatically detect subtle instability using algorithms. [Abstract copyright: Copyright © 2018. Published by Elsevier B.V.

Task scheduling to constrain peak current consumption in wearable healthcare sensors

Small embedded systems, in our case wearable healthcare devices, have significant engineering challenges to reduce their power consumption for longer battery life while at the same time supporting ever increasing processing requirements for more intelligent applications. Research has primarily focused on achieving lower power operation through hardware designs and intelligent methods of scheduling software tasks, all with the objective to minimize the overall consumed electrical power. However, such an approach inevitably creates points in time where software tasks and peripherals coincide to draw large peaks electrical current creating short-term electrical stress for the battery and power regulators, and adding to electromagnetic interference emissions. This position paper proposes that the power profile of an embedded device using a Real-Time Operating System (RTOS) will significantly benefit if the task scheduler is modified to be informed of the electrical current profile required for each task. This enables the task scheduler to schedule tasks that require large amounts of current to be spread over time, thus constraining the peak current that the system will draw. We propose a solution to inform the task scheduler of a tasks’ power profile and we discuss our application scenario that clearly benefited from the proposal