Squat is known as the king of strength trainings. However, the incorrect positions may produce irreversible damage to the human body. This study proposes a method utilizing plantar pressure to detect common incorrect squats position. Insoles with eight pressure sensors are utilized to collected 5 sets of database, which are correct squat and 4 common incorrect squats. An algorithm is proposed to segment those continuous pressure data. Then, the pressure nephogram is also analyzed. Three sets of deep neural network are designed as classifiers, which are Att-LSTM, LSTM, and CNN, respectively. Experimental results show that accuracies of these models are 90. 2% , 83. 0% and 79. 8% , respectively. The results suggest that the utilization of sensor insoles with LSTM and attention mechanism as the classification algorithm is a valid method to detect squats position.