Caregivers experience lower back pain due to their awkward postures while handling patients. Therefore, a monitoring system to supervise caregivers' postures using wearable sensors is being developed. This study proposed a postural recognition method for caregivers during postural change while handling a patient on a bed. The proposed method recognizes foot positions and arm movements by a machine learning algorithm using inertial data on the trunk and foot pressure data obtained from wearable sensors. An experiment was conducted to evaluate whether the proposed method could recognize three foot positions and three arm movements. Participants provided postural change for a simulated patient on a bed (patient: supine to lateral recumbent) under nine conditions, including different combinations of the three foot positions and three arm movements; the experiment was repeated ten times for each condition. Experimental results showed that the proposed method using an artificial neural network with all features obtained from an inertial measurement unit and insole pressure sensors could recognize arm movements and foot positions with an accuracy of approximately 0.75 and 0.97, respectively. These results suggest that the proposed method can be used in a monitoring system tracking a caregiver's posture.