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Background Repetitive, flexed lumbar postures are a risk factor associated with low back injuries. Young, novice workers involved in manual handling also appear at increased risk of injury. The evidence for the effectiveness of postural biofeedback as an intervention approach is lacking, particularly for repetitive, fatiguing tasks. Research question How does real-time lumbosacral (LS) postural biofeedback modify the kinematics and kinetics of repetitive lifting and the risk of low back injury? Methods Thirty-four participants were randomly allocated to two groups: biofeedback (BF) and non-biofeedback (NBF). Participants repetitively lifted a 13 kg box at 10 lifts per minute for up to 20 min. Real-time biofeedback of LS posture occurred when flexion exceeded 80% maximum. Three-dimensional motion analysis and ground reaction forces enabled estimates of joint kinematics and kinetics. Rating of perceived exertion (RPE) was measured throughout. Results The BF group adopted significantly less peak lumbosacral flexion (LSF) over the 20 min when compared to the NBF group, which resulted in a significant reduction in LS passive resistance forces. This was accompanied by increased peak hip and knee joint angular velocities in the BF group. Lower limb moments did not significantly differ between groups. Feedback provided to participants diminished beyond 10 min and subjective perceptions of physical exertion were lower in the BF group. Significance Biofeedback of lumbosacral posture enabled participants to make changes in LSF that appear beneficial in reducing the risk of low back injury during repetitive lifting. Accompanying behavioural adaptations did not negatively impact on physical exertion or lower limb joint moments. Biofeedback of LS posture offers a potential preventative and treatment adjunct to educate handlers about their lifting posture. This could be particularly important for young, inexperienced workers employed in repetitive manual handling who appear at increased risk of back injury

In this study, performance of different wind turbines is compared with using QBlade software. Normally wind turbines are categorized as Horizontal Axis Wind Turbine (HAWT) and Vertical axis wind turbine (VAWT) which in this paper Darrieus type as a VAWT is discussed with HAWT. The airfoil selected for this study is NACA 0012 and from simulations it can be resulted how adding blades influence efficiency in wind turbines. In terms of using wind turbines, number of blades is one of the important parameters. QBlade contains the XFOIL airfoil analysis functionalities which makes a software a single tool that comprises all functionality needed for the design and simulation of vertical and horizontal axis wind turbines. The benefits of the proper airfoil design optimal is increasing the efficiency of wind turbines and maximization of the power produced.

This article presents an ambience intelligence application for smart home systems for efficient use of electricity, enhance comfort zone, independence of living and security. The system integrates smart home occupant's identification, sensors-actuators deployment, a gateway Hub, machine learning, and cloud computing components to realize the objectives of smart living. A Mobile phone MAC-address based occupant identification and machine learning algorithms are proposed to address the multiple smart home occupant problems and an instant user control versus rule-based control conflicts. Results show that machine learning algorithm could learn the ambience preferences of multiple occupants when trained on a large enough dataset. The proposed system can implement ambient intelligence applications in smart home.

The paper presents research that investigated the Life Cycle Assessment of multi-crystalline photovoltaic (PV) panels, by considering environmental impacts of the entire life cycle for any solar PVsystems. The overall manufacturing process of a solar PV panel ranging from silica extraction, crystalline silicon ingot growth, wavering to module fabrication and packing of the solar PV panel. The results from this research showed that the module assembly and cell processing of the manufacturing process contributed towards the main environmental impacts of the life cycle of solar PV systems.

This paper evaluates the feasibility of using a hybrid system consisting of wind and tidal turbines connected to a microgrid for power supply to coastal communities that are isolated from a main supply grid. The case study is Stewart Island, where the cost of electricity, provided by a central diesel power station, is higher than the grid network in New Zealand. Local residents believe that reducing the consumption of diesel and having a renewable source of electricity generation are two of the island’s highest priorities. Merging a tidal energy source (predictable) with wind (unpredictable) and diesel (back-up), through a microgrid, may be a way to increase reliability and decrease the cost of generation. Several off-grid configurations are simulated using HOMER and WRPLOT software. Using two wind and four tidal turbines, plus one diesel generator for back-up, is the best design in terms of lower greenhouse gas emissions, higher renewable fraction, and reduced net present cost.

The benefits of the rotor design are optimizing power generation and reducing cost of build in wind turbines. In this study, a performance comparison of horizontal axis wind turbines in terms of their rotor diameter is made using Qblade software. The airfoil selected is the National Advisory Committee for Aeronautics (NACA 0012) for all models, and the wind turbines have three blades. From simulations, the optimal rpm for each rotor diameter was calculated, and the generated power for different wind speeds. The results found show that the operation of wind turbines consider need operated with tip speed ratios of between 4 to 7 to give optimal power output. Maximum power was produced for a blade diameter of 170 metres and the highest power coefficient found was for a diameter of 30 metres and for this case also had the highest tip speed ratio of 6.68. Overall results can suggest which diameter range is used for choosing a rotor size that is the most cost-effective.

This research focuses on proposing and evaluating an optimized hybrid system of wind and tidal turbines operating as a renewable energy generating unit in New Zealand. Literature review indicates increasing worldwide investment in offshore renewable energy in recent years. Offshore energy shows a high potential as an alternative energy generation solution to that of fossil fuels. Using the capacities of wind and tidal power in renewable technologies would be a suitable alternative for fossil fuels and would help prevent their detrimental effects on the environment. It is a cost-effective procedure for the power generation sector to maximize these renewables as a hybrid system. At the design phase, turbine types appropriate to environmental conditions for an area with high wind speed and tidal flow need to be considered. When selecting which turbines should be used, horizontal or vertical axis, number and length of blades, and optimized rotational speed are all important to get maximum capacity from either the wind or tidal energy for the hybrid system. Comprehensive simulation models of the hybrid system are now being set up, using several available commercial software packages such as QBlade, Simulink, and RETScreen. Several different parameters will be required for these simulation models to run in order to test performance, capacity and efficiency of the proposed hybrid system. To decide which regions are suitable for the hybrid system, it will be necessary to analyze available wind and tide records from NIWA, and online databases such as GLOBAL ATLAS. This next phase of research will aim to create optimized scenarios for the hybrid model by considering the effect of wind and water speed on performance. After deciding which region and scenarios are suitable, it will also be necessary to evaluate the costs and returns of a hybrid system. This final phase will be performed using the RETScreen simulation model.

Introduction Low back pain (LBP) is the leading cause of disability globally. Occupational risk factors associated with LBP include heavy, repetitive lifting, and awkward and flexed postures. Studies suggest younger people may be at an increased risk of LBP compared to older individuals. Postural feedback as an early intervention may provide a viable preventative approach for reducing the risk of LBP in the workplace. Methods Thirty-four healthy, young participants were randomly allocated biofeedback (BF) or no biofeedback (NBF). Participants lifted a box weighing 13 kg at a frequency of 10 lifts per minute for up to 20 min. Real-time biofeedback on lumbar posture was provided using two wireless inertial sensors attached to the lumbar spine and sacrum. The BF group received an audible cue when lumbar flexion exceeded 80% maximum flexion. Three-dimensional motion analysis and ground reaction forces were used to estimate moments at the base of the spine and on the passive structures of the lumbar spine. Participants rated perceived exertion throughout the task using Borg’s scale. Results Both the BF and NBF groups increased lumbar flexion over the duration of the lifting task, although the rate of change (slope) of peak lumbar flexion with BF was significantly less compared to NBF (p=0.009). Normalised bending moments resisted by passive structures of the spine were higher in the NBF group (0.7 Nm/kg) compared to the BF group (0.12 Nm/kg) at 20 min. The BF group demonstrated lower levels of perceived exertion. Discussion Real-time biofeedback provides a viable approach to increase spinal postural awareness during repetitive lifting tasks, helping to reduce loads on the passive structures of the lumbar spine. A simple wireless inertial system for monitoring posture has potential for use in the work environment, and further evaluations should be undertaken to determine the long-term effectiveness of this approach.

This paper presents findings from a study of five healthy subjects performing 50% maximum voluntary contraction until complete fatigue of the muscle. An overlapping window technique was used to find the values for mean frequency (MNF), median frequency (MDF) of the power spectrum, root mean square (RMS) and muscle fibre conduction velocity (MFCV). The surface electromyography signal (sEMG) was collected from the vastus lateralis muscle using a three channel Laplacian electrode. The results show the MNF and MDF values showed a consistent trend with each other where they remained at steady values between 20-30% and 75-80% of the signal after which they fell 15-30% of this value. The RMS showed a linear increase in value. The MFCV showed a similar trend to that found in the MNF and MDF values. 

This paper covers the design aspects of a new multi-channel electrode for the acquisition of surface electromyography signals from a selected muscle. The new multi-channel electrode has 11 pins where the monopolar signals produced will be configured in a software either as Linear array or Laplacian configuration. The design specification of the pre-amplifier ideally was to have a voltage gain of 500 with bandpass filtering of 5 Hz-1 kHz. The final design of the pre-amplifier circuit using an INA 118 instrumentation amplifier was built and tested to give values for voltage gain of 484 with bandpass filtering of 6.8 Hz-1.02 kHz. The software configuration that gives clearer and more defined signals in terms of motor unit action potentials for future signal processing is the Laplacian rather than Linear array.

Conventional collection of surface electromyography signals requires the surface electrodes, amplifier and data acquisition card to all be wired together, restricting the movement of the test subject. With the advancement of wireless technology such as Bluetooth and XBee with ZigBee, the wire link between the electrodes and computer storing the data can be removed. Using Bluetooth configuration will require new circuitry of an analogue-to-digital convertor with a microcontroller to a Bluetooth module with the wireless link to an USB Bluetooth modem dongle. The XBee configuration will also require an analogue-to-digital convertor with a microcontroller to a XBee transceiver for the wireless link to an USB XBee dongle transceiver. XBee with ZigBee configuration will allow multichannel collection of signals as a wireless sensor network.

The data collection of surface electromyography signals has required the sensor, amplifier and data acquisition card to be all wired together restricting the movement of the subject being tested. With the advancement of wireless technology such as Bluetooth, IEEE 802.15.4 and ZigBee the wire link between the amplifier and computer analysing and storing the data can be removed. Using Bluetooth will require new circuitry of a microcontroller with an analogue-digital convertor, a RS232 serial link, transceiver and a USB dongle transceiver for the wireless link. The IEEE 802.15.4 will require a single chip microcontroller to perform the analogue-digital convertor and also a transceiver for the wireless link to an USB dongle transceiver. ZigBee will build on the IEEE 802.15.4 configuration to allow multichannel collection of signals as a wireless sensor network.

To date various signal processing techniques have been applied to surface electromyography (SEMG) for feature extraction and signal classification. Compared with traditional analysis methods which have been used in previous application, continuous wavelet transform (CWT) enhances the SEMG features more effectively. This paper presents methods of analysing SEMG signals using CWT and LabVIEW for extracting accurate patterns of the SEMG signals. We used the scalogram and frequency-time based spectrum to plot the power of the wavelet transform and enhance the diagnosis features of the signal. As a result, clinical interpretation of SEMG can be improved by extracting time-based information as well as scales, which can be converted to frequencies. Using the extracted features of the dominant frequencies of the wavelet transform and the related scales, we were able to train and validate an artificial neural network (ANN) for SEMG classification.

A number of Digital Signal Processing (DSP) techniques are being applied to Surface Electromyography (SEMG) for classification using signal feature extraction. This research is aimed at using Continuous Wavelet Transform (CWT) analysis of SEMG signals to develop and adopt a sound methodology for classification of the signals at different force levels. Traditional analysis methods such as Fast Fourier Transform (FFT) could not be used alone because muscle diagnosis requires time-based information. Therefore CWT was selected for this research as it includes time-based information as well as scales that can be converted into frequencies, making muscle diagnosis easier. CWT produces a scalogram plot along with its corresponding time-based frequency spectrum plot. Using both of these plots, extracted features of the dominant frequencies and the related scales can be used to train and validate a signal classifier based on an Artificial Neural Network (ANN). SEMG signals were obtained for a 10 second period sampled at 2048 Hz and digitally filtered using a Butterworth bandpass filter (5 to 500 Hz, 4th order). Signals were collected from the vastus medialis muscle of both legs of 45 healthy male subjects at 25%, 50% and 75% of their Maximum Voluntary Isometric Contraction (MVIC) force of the quadriceps. The extracted features selected for the two second period of the signal were the mean and median frequencies of the average power spectrum, and the RMS values at scale 8, 16, 32, 64 and 128 of the scalogram. The signals were analysed using CWT in LabVIEW with its Signal Processing Toolset. In this paper, the results are presented to show that the selected extracted features are suitable for the classification of SEMG signals at different force levels.

A number of digital signal processing (DSP) techniques are being applied to surface electromyography (SEMG) signals to extract detailed features of the signal. Fast Fourier transform (FFT) is one of the most common methods for analyzing the signal whether it is filtered or not. Another DSP technique is referred to as wavelet analysis, a method that is gaining more use in analyzing SEMG signals. This research focuses on using the discrete wavelet transform (DWT) and the wavelet package transform (WPT). Both DWT and WPT use analytical wavelets called "mother wavelet" which comes in different sets or "families". Wavelet analysis has the advantage over FFT as it provides the frequency contents of the signal over the time period that is being analyzed. SEMG signals were collected from a muscle under sustained contractions for 4 seconds with different loads. The raw signals were analyzed using FFT, DWT and WPT in LabVIEW/spl reg/ using its signal processing toolset. Using wavelet analysis the SEMG signal was decomposed into its frequency content form and then was reconstructed. In this paper the results are presented to show that certain families of mother wavelets of wavelet analysis are more suitable than others for analyzing SEMG signals.

A number of digital signal processing techniques are being applied to surface electromyography (SEMG) signals for classification using feature extraction. Traditional analysis methods such as fast Fourier transform (FFT) could not be used alone because muscle diagnosis requires time-based information. Continuous wavelet transform (CWT) was selected for extracting efficient features of the SEMG signals in this research. CWT includes time-based information as well as scales, which can be converted to frequencies, making muscle diagnosis easier. CWT produces a scalogram plot along with its corresponding time-frequency based spectrum plot. Using the extracted features of the dominant frequencies of the wavelet transform and the related scales, we were able to train and validate an artificial neural network (ANN) for signal classification. (4 pages)

This paper discusses implementing a Location-Aware Library RFID service employing Radio Frequency Identification as a communication technology. Automating a library system with passive RFID tag infrastructure is proven to be feasible, and can be achieved with even distribution of RFID Antennas, well designed RFID network, an appropriate middleware, and a library application with an accurate error function that minimizes the error in the detected location to an acceptable distance, for instance, 35 cm. The paper first discusses the effect of using RFID on the upper application and System layers. A simulation of randomly distributed tags with spatially distributed antennas is shown. Simulation results are provided to prove the validity of the proposed approach, which proves that adding a localization feature to the system layer is feasible with an acceptable error range that does not exceed 1.1%.