Why I left and why I want to leave: A Phenomenological Perspective of Asian Employees Turnover

The current research was conducted to explore the possible causes of actual employee turnover and turnover intentions. Using Post positivism research philosophy, phenomenological qualitative research method was used to explore the phenomena. Semi-structured interviews of 21 bank employees (selected using purposive sampling) were conducted which were analyzed using NVivo 12. The research findings suggest many uniques themes in order to overcome the problem of employee turnover, especially for banks. The themes which were developed consisted of five significant themes such as the bank appraisals and reward system was identified as biased and based more on favoritism, employee feel that their actual performance is not evaluated properly and sincerely. The other factor concluded by the research findings is that the employees are dissatisfied with the salary and benefits, as they felt that there should a consistent effort to identify employee personal needs which should be customized accordingly in their compensation plans as well. The very essential factor recognized in the research finding was the upward and downward communication gaps with the employees. Such perceptions generated related issues as the employees felt that branches are much deprived to have a direct communication channel with the top team heads. The other very essential factor discovered after the investigation of the phenomena of turnover is lack of career growth. Lastly, another important cause of employee turnover was the transfers, which took place without the consent of the employee. Employees felt demotivated due to such transfers and changes in their work locations. Recommendations and future research directions have been at the end of the research

Deep Learning for Improved Myoelectric Control

Advancement in the myoelectric interfaces have increased the use of myoelectric controlled robotic arms for partial-hand amputees as compared to body-powered arms. Current clinical approaches based on conventional (on/off and direct) control are limited to few degree of freedom (DoF) movements which are being better addressed with pattern recognition (PR) based control schemes. Performance of any PR based scheme heavily relies on optimal features set. Although, such schemes have shown to be very effective in short-term laboratory recordings, but they are limited by unsatisfactory robustness to non-stationarities (e.g. changes in electrode positions and skin-electrode interface). Moreover, electromyographic (EMG) signals are stochastic in nature and recent studies have shown that their classification accuracies vary significantly over time. Hence, the key challenge is not the laboratory short term conditions but the daily use. Thus, this work makes use of the longitudinal approaches with deep learning in comparison to classical machine learning techniques to myoelectric control and explores the real potential of both surface and intramuscular EMG in classifying different hand movements recorded over multiple days. To the best of our knowledge, for the first time, it also explores the feasibility of using raw (bipolar) EMG as input to deep networks. Task are completed with two different studies that were performed with different datasets. In the first study, surface and intramuscular EMG data of eleven wrist movements were recorded concurrently over six channels (each) from ten able-bodied and six amputee subjects for consecutive seven days. Performance of stacked sparse autoencoders (SSAE), an emerging deep learning technique, was evaluated in comparison with state of art LDA using offline classification error as performance matric. Further, performance of surface and intramuscular EMG was also compared with respect to time. Results of different analyses showed that SSAE outperformed LDA. Although there was no significant difference found between surface and intramuscular EMG in within day analysis but surface EMG significantly outperformed intramuscular EMG in long-term assessment. In the second study, surface EMG data of seven able-bodied were recorded over eight channels using Myo armband (wearable EMG sensors). The protocol was set such that each subject performed seven movements with ten repetitions per session. Data was recorded for consecutive fifteen days with two sessions per day. Performance of convolutional neural network (CNN with raw EMG), SSAE (both with raw data and features) and LDA were evaluated offline using classification error as performance matric. Results of both the short and long-term analyses showed that CNN and SSAE-f outperformed the others while there was no difference found between the two. Overall, this dissertation concludes that deep learning techniques are promising approaches in improving myoelectric control schemes. SSAE generalizes well with hand-crafted features but fails to generalize with raw data. CNN based approach is more promising as it achieved optimal performance without the need to select features.