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.

[1]  Dympna O'Sullivan,et al.  Real-world Gyroscope-based Gait Event Detection and Gait Feature Extraction , 2014, eTELEMED 2014.

[2]  Suraj Raghuraman,et al.  Exploring unconstrained mobile sensor based human activity recognition , 2013 .

[3]  Katarzyna Radecka,et al.  Affordable erehabilitation monitoring platform , 2014, 2014 IEEE Canada International Humanitarian Technology Conference - (IHTC).

[4]  Christoph Busch,et al.  Classifying accelerometer data via Hidden Markov Models to authenticate people by the way they walk , 2011, 2011 Carnahan Conference on Security Technology.

[5]  Gerhard Tröster,et al.  The adARC pattern analysis architecture for adaptive human activity recognition systems , 2011, Journal of Ambient Intelligence and Humanized Computing.

[6]  Sinziana Mazilu,et al.  GaitAssist: a daily-life support and training system for parkinson's disease patients with freezing of gait , 2014, CHI.

[7]  Diane J. Cook,et al.  Activity recognition on streaming sensor data , 2014, Pervasive Mob. Comput..

[8]  Lin Sun,et al.  Activity Recognition on an Accelerometer Embedded Mobile Phone with Varying Positions and Orientations , 2010, UIC.

[9]  Rémy Courdier,et al.  A model to represent human activities in farming systems based on reactive situated agents , 2011 .

[10]  Hongnian Yu,et al.  Activity classification using a single wrist-worn accelerometer , 2011, 2011 5th International Conference on Software, Knowledge Information, Industrial Management and Applications (SKIMA) Proceedings.

[11]  Paul Lukowicz,et al.  Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers , 2004, Pervasive.

[12]  Michael L. Littman,et al.  Activity Recognition from Accelerometer Data , 2005, AAAI.

[13]  Diogo R. Ferreira,et al.  Preprocessing techniques for context recognition from accelerometer data , 2010, Personal and Ubiquitous Computing.

[14]  Christoph Busch,et al.  Using Hidden Markov Models for accelerometer-based biometric gait recognition , 2011, 2011 IEEE 7th International Colloquium on Signal Processing and its Applications.

[15]  Bhushan G. Jagyasi,et al.  Mobile sensing for agriculture activities detection , 2013, 2013 IEEE Global Humanitarian Technology Conference (GHTC).

[16]  Franklin W. Olin,et al.  Detecting User Activities using the Accelerometer on Android Smartphones , 2010 .

[17]  Sung-Bae Cho,et al.  Activity Recognition Using Hierarchical Hidden Markov Models on a Smartphone with 3D Accelerometer , 2011, HAIS.

[18]  Gang Zhou,et al.  Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information , 2009, 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks.

[19]  Bhushan G. Jagyasi,et al.  Design and development of wearable sensor textile for precision agriculture , 2015, 2015 7th International Conference on Communication Systems and Networks (COMSNETS).

[20]  Ilkka Korhonen,et al.  Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions , 2008, IEEE Transactions on Information Technology in Biomedicine.

[21]  Cem Ersoy,et al.  Online Human Activity Recognition on Smart Phones , 2012 .

[22]  Diane J. Cook,et al.  Activity Discovery and Activity Recognition: A New Partnership , 2013, IEEE Transactions on Cybernetics.

[23]  Kentaro Toyama,et al.  Digital Green , .

[24]  Frank Sposaro,et al.  iFall: An android application for fall monitoring and response , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[25]  Diane J. Cook,et al.  Simple and Complex Activity Recognition through Smart Phones , 2012, 2012 Eighth International Conference on Intelligent Environments.