AgriAcT: Agricultural Activity Training using multimedia and wearable sensing

There has been immense work in past on the human activities detection and context recognition using the wearable sensing technologies. However, a more challenging problem of providing training on the activities to the users with the help of wearable sensors has not been adequately attempted. Specially, in the agriculture applications, an appropriate training to the farmers on performing the agricultural activities would result in the sustainable agriculture practices for achieving higher and better quality yield. In this paper, a novel first-of-a-kind, multimedia and wearable sensors based Agricultural Activity Training (AgriAcT) system has been proposed for the dissemination of agricultural technologies to the remotely located farmers. In the proposed system, a training video of an expert farmer performing an activity is captured along with the gesture data obtained from the wearable motion sensors from the expert's body while the activity is being performed. A trainee farmer, can learn a selected activity by watching the multimedia content of the expert performing that activity on the mobile phone and subsequently perform the activity by wearing the required motion sensors. We present a novel K-Nearest Neighbor based Agriculture Activity Performance Score (KAAPS) engine to generate an Activity performance score (AcT-Score) which suggest how efficiently the activity had been performed by the trainee as compared to the expert's performance. The exhaustive experimental results by collecting data from eight experts and ten trainees for two different activities are used to present the inferences on the impact made by the Act-Score on the performance of the trainee farmers.