Risk factors, such as overexertion, awkward postures, excessive repetition, and the combination of these factors are main causes of work-related musculoskeletal disorders (WMSDs) which are reported to be the leading nonfatal occupational injuries. However, it is difficult for commonly used risk factors identification methods (e.g. observational methods) to give an objective and comprehensive analysis on these risk factors. To address the above problem, we proposed an automatic WMSDs risk factors identification method based on Wearable and Connected Gait Analytics System (WCGAS). WKGAS was capable of recording plantar pressure from which postures, force exertions, and repetitions could be recognized with algorithms such as sequential minimal optimization (SMO) algorithm and long short term memory (LSTM) network. Experiments with quasi-static and sequential postures were designed to evaluate the recognition performance of work-related motion type (i.e. "lifting", "carrying", "bending", "pulling", and "pushing"). A load variable (with/without 10 Kg load) was introduced for evaluating the performance of force exertions recognition. 5 repetitions of each motion were used for evaluating the performance of repetitions recognition. Results showed that quasi-static postures could be recognized with 100% accuracy and the accuracy for sequential motions recognition were 74%, 79%, 92%, 99% and 99% for "bending", "carrying", "lifting", "pulling" and "pushing", respectively. Force exertions were recognized with 100% accuracy. For repetitions recognition, except the accuracy in the "bending" motion was 80%, the repetitions of other motions could be recognized correctly. These results indicated that it is possible to use WCGAS for WMSDs risk factors identification.