Musculoskeletal disorders (MSDs) caused by muscle fatigue have been a major problem for industry
which needs to be resolved to save costs related to human resource development (extra training and
compensation). Detailed fatigue monitoring researches aimed at finding the best fatigue indices is not
new although studies on the causes of fatigue can be explored further. Identification analysis is required
to monitor the factors that influence muscle performance characteristic of surface electromyography
(sEMG) signal. Periodogram monitoring technique applies a frequency domain signal and represents the
distribution of the signal power over the frequency. It is a technique that allows the tracing of small
changes in the behaviour of sEMG signal when external parameters are varied. This technique is used
in this paper to monitor the sEMG signal changes in muscle performance when the lifting height and
load mass are varied. The periodogram amplitude, which represents the power, increases with the rise in
lifting height and load mass. From the frequency representation of the periodogram, the root mean square
voltage (Vrms) is calculated where the muscle performance characteristic could be further identified. The
Vrms also shows a similar trend when the lifting height and load mass are varied proving the periodogram
technique is useful to monitor changes in the muscle performance during manual lifting.
Manual lifting is one of the common practices used in the industries to transport or move objects to a desired place. Nowadays, even though mechanized equipment is widely available, manual lifting is still considered as an essential way to perform material handling task. Improper lifting strategies may contribute to musculoskeletal disorders (MSDs), where overexertion contributes as the highest factor. To overcome this problem, electromyography (EMG) signal is used to monitor the workers' muscle condition and to find maximum lifting load, lifting height and number of repetitions that the workers are able to handle before experiencing fatigue to avoid overexertion. Past researchers have introduced several EMG processing techniques and different EMG features that represent fatigue indices in time, frequency, and time-frequency domain. The impact of EMG processing based measures in fatigue assessment during manual lifting are reviewed in this paper. It is believed that this paper will greatly benefit researchers who need a bird's eye view of the biosignal processing which are currently available, thus determining the best possible techniques for lifting applications.
Medical imaging techniques have widely been in use in the diagnosis and detection of breast cancer. The drawback of applying these techniques is the large time consumption in the manual diagnosis of each image pattern by a professional radiologist. Automated classifiers could substantially upgrade the diagnosis process, in terms of both accuracy and time requirement by distinguishing benign and malignant patterns automatically. Neural network (NN) plays an important role in this respect, especially in the application of breast cancer detection. Despite the large number of publications that describe the utilization of NN in various medical techniques, only a few reviews are available that guide the development of these algorithms to enhance the detection techniques with respect to specificity and sensitivity. The purpose of this review is to analyze the contents of recently published literature with special attention to techniques and states of the art of NN in medical imaging. We discuss the usage of NN in four different medical imaging applications to show that NN is not restricted to few areas of medicine. Types of NN used, along with the various types of feeding data, have been reviewed. We also address hybrid NN adaptation in breast cancer detection.