Due to its inherent complexity such as limited work volume and degree of freedom, minimally invasive surgery (MIS) is ergonomically challenging to surgeons than traditional open surgery. Specifically, MIS can expose performing surgeons to excessive ergonomic risks including muscle fatigue that may lead to critical errors in surgical procedures. Therefore, detecting the vulnerable muscles and time-to-fatigue during MIS is of great importance in order to prevent these errors. In this research, different surgical skill and ergonomic assessment methods are reviewed and their advantages and disadvantages are studied. According to the literature review, which is included in chapter 1, some of these methods are subjective and those that are objective provide inconsistent results. Muscle fatigue analysis has shown promising results for skill and ergonomic assessments. However, due to the data analysis issues, this analysis has only been successful in intense working conditions. The goal of this research is to apply an appropriate data analysis method to minimally invasive surgical setting which is considered as a low-force muscle activity. Therefore, surface electromyography is used to record muscle activations of subjects while they performed various real laparoscopic operations and dry lab surgical tasks. The muscle activation data is then reconstructed using Recurrence Quantification Analysis (RQA), which has been proven to be a reliable analysis, to detect possible signs of muscle fatigue on different muscle groups. The results of this data analysis method is validated using subjective fatigue assessment method. In order to study the effect of muscle fatigue on subject’s performance, standard Fundamental of Laparoscopic Surgery (FLS) tasks performance analysis is used.
| Identifer | oai:union.ndltd.org:siu.edu/oai:opensiuc.lib.siu.edu:dissertations-2324 |
| Date | 01 December 2016 |
| Creators | Panahi, Ali |
| Publisher | OpenSIUC |
| Source Sets | Southern Illinois University Carbondale |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Dissertations |
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