In Parkinson's disease (PD), on-demand deep brain stimulation is required so that stimulation is regulated to reduce side effects resulting from continuous stimulation and PD exacerbation due to untimely stimulation. Also, the progressive nature of PD necessitates the use of dynamic detection schemes that can track the nonlinearities in PD. This paper proposes the use of dynamic feature extraction and dynamic pattern classification to achieve dynamic PD detection taking into account the demand for high accuracy, low computation, and real-time detection. The dynamic feature extraction and dynamic pattern classification are selected by evaluating a subset of feature extraction, dimensionality reduction, and classification algorithms that have been used in brain-machine interfaces. A novel dimensionality reduction technique, the maximum ratio method (MRM) is proposed, which provides the most efficient performance. In terms of accuracy and complexity for hardware implementation, a combination having discrete wavelet transform for feature extraction, MRM for dimensionality reduction, and dynamic k-nearest neighbor for classification was chosen as the most efficient. It achieves a classification accuracy of 99.29%, an F1-score of 97.90%, and a choice probability of 99.86%.

Clinical deep brain stimulation (DBS) is now regarded as the therapeutic intervention of choice at the advanced stages of Parkinson's disease. However, some major challenges of DBS are stimulation induced side effects and limited pacemaker battery life. Side effects and shortening of pacemaker battery life are mainly as a result of continuous stimulation and poor stimulation focus. These drawbacks can be mitigated using adaptive DBS (aDBS) schemes. Side effects resulting from continuous stimulation can be reduced through adaptive control using closed-loop feedback, while those due to poor stimulation focus can be mitigated through spatial adaptation. Other advantages of aDBS include automatic, rather than manual, initial adjustment and programming, and long-term adjustments to maintain stimulation parameters with changes in patient's condition. Both result in improved efficacy. This review focuses on the major areas that are essential in driving technological advances for the various aDBS schemes. Their c...

Purpose: – The aim of this paper is to introduce and to evaluate the performance of a multiple frequency complex impedance reconstruction for fabric-based EIT pressure sensor. Pressure mapping is an important and challenging area of modern sensing technology. It has many applications in areas such as artificial skins in Robotics and pressure monitoring on soft tissue in biomechanics. Fabric-based sensors are being developed in conjunction with electrical impedance tomography (EIT) for pressure mapping imaging. This is potentially a very cost-effective pressure mapping imaging solution in particular for imaging large areas. Fabric-based EIT pressure sensors aim to provide a pressure mapping image using current carrying and voltage sensing electrodes attached on the boundary of the fabric patch. Design/methodology/approach: – Recently, promising results are being achieved in conductivity imaging for these sensors. However, the fabric structure presents capacitive behaviour that could also be exploited for pressure mapping imaging. Complex impedance reconstructions with multiple frequencies are implemented to observe both conductivity and permittivity changes due to the pressure applied to the fabric sensor. Findings: – Experimental studies on detecting changes of complex impedance on fabric-based sensor are performed. First, electrical impedance spectroscopy on a fabric-based sensor is performed. Secondly, the complex impedance tomography is carried out on fabric and compared with traditional EIT tank phantoms. Quantitative image quality measures are used to evaluate the performance of a fabric-based sensor at various frequencies and against the tank phantom. Originality/value: – The paper demonstrates for the first time the useful information on pressure mapping imaging from the permittivity component of fabric EIT. Multiple frequency EIT reconstruction reveals spectral behaviour of the fabric-based EIT, which opens up new opportunities in exploration of these sensors.

For safe operation of a vehicle, the driver must be able to maintain absolute control of the vehicle’s serious operating dynamics. To achieve this, extensively power steering systems have been employed today. This has been as a result of the increase in the front axle loads of vehicles, making it require more power to turn its wheels. In addition, there is a need for vehicles with more responsive steering properties. As a result, the quest for higher efficiency of the system necessitates the need for a more flexible and efficacious system. Electric Power-Assisted Steering (EPAS) systems can serve as alternatives to hydraulic power-assisted steering systems. The EPAS system offered the desired flexibility, engine and space efficiency. In addition, no pollution is emitted to the environment because of the omission of all hydraulic components. However, it has issues with system inertia and vehicle stability. Therefore, the requirement for vehicle steering stability becomes strict. As such, the focus of this paper is to give an overview of the EPAS system, its different configurations and subsystems, in addition to prospects and challenges of adopting the technology in vehicles.

The progress of Parkinson’s disease (PD) in patients is conventionally monitored through follow-up visits. These may be insufficient for clinicians to obtain a good understanding of the occurrence and severity of symptoms in order to adjust therapy to the patients’ needs. Portable platforms for PD diagnostics can provide in-depth information, thus reducing the frequency of face-to-face visits. This paper describes the first known on-site PD detection and monitoring processor. This is achieved by employing complementary detection which uses a combination of weak k-NN classifiers to produce a classifier with a higher consistency and confidence level than the individual classifiers. Various implementations of the classifier are investigated for trade-offs in terms of area, power and detection performance. Detection performances are validated on an FPGA platform. Achieved accuracy measures were: Matthews correlation coefficient of 0.6162, mean F1-score of 91.38%, and mean classification accuracy of 91.91%. By mapping the implemented designs on a 45 nm CMOS process, the optimal configuration achieved a dynamic power per channel of 2.26 μW and an area per channel of 0.24 mm2.

Unmanned aerial vehicles (UAV) have applications in military, reconnaissance, disaster management, communication support, geographical survey and photography among others. Their use in everyday life is increasing. A common shortcoming of UAV is limited endurance. Most UAVs make use of conventional fuel for their propulsion as well as for powering on-board equipment. Relative to the size of these UAVs, the amount of fuel they can carry limits their endurance. Hence, there is a need for alternative ways to enhance their endurance as well to free the environment from pollutants. Sub-Saharan Africa is endowed with abundant solar radiation in which solar-powered UAVs have the potential to achieve optimum flight performance. Therefore, this paper is focused on providing insight on the prospects of using a solar-powered UAV in Sub-Saharan Africa to achieve optimum flight performance.