Impulsive Behaviour Detection System Using Machine Learning and IoT

Abstract

Wearable devices equipped with a multitude of compact, lightweight, biometric sensors are helpful in tracking the real-time physiological data for healthcare-related analysis. However, a survey of devices under the smart-wearable market segment revealed that the contemporary focus is limited to capturing and displaying some of the biometrics like pulse rate, movement of the user, calorie counter, etc. on a smart screen. Employing machine learning techniques can be particularly helpful in analyzing the trends of user-specific biometric data for pre-emptive actions. This paper presents a meaningful analysis in real-time by using machine learning approaches to interpret this physiological data and alert the user about his behavioral pattern through IoT. This research focuses on classifying the mood of the user as agitated or non-agitated, by analyzing the biometrics to help the user decipher meaningful conclusions and take suitable pre-emptive measures to refrain from any unintentional impulsive outburst. Machine learning algorithms like Polynomial regression with threshold, Decision Tree, Random Forest ensemble and variants of Deep Neural Networks (DNN) have been employed to analyse the biometric patterns from the experimental data acquired under different circumstances and detect the user’s mood to assign a score to the user. The proposed approach uses a reinforcement learning algorithm to calibrate the user’s current temperament by taking intermediate user feedback and comparing the score with the temperament. The results reveal that the proposed system detects the user’s mood fluctuations with higher accuracy and relevance compared to any contemporary model.

Keywords:

• Artificial intelligence
• Biometrics
• Decision tree
• Neural network
• Polynomial regression
• Random forest
• Reinforcement learning

ACKNOWLEDGEMENTS

We express our sincere gratitude and thankfulness to Dr. Pallaviram Sure, Associate Professor, Dept. of ECE, MSRUAS for her immense help and support in setting up the hardware.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Soumya Jyoti Raychaudhuri

Soumya Jyoti Raychaudhuri received his BTech from the West Bengal University of Technology and currently pursuing MTech in machine learning and intelligent systems at the Department of Computer Sciences and Engineering, Ramaiah University of Applied Sciences, Bangalore, India. His research interests include data analytics, predictive analytics, optimization, computer vision, machine learning and deep learning algorithms. Email: [email protected]

Soumya Manjunath

Soumya Manjunath received her BE degree in ECE from MS Ramaiah Institute of Technology and MTech in MLIS from MS Ramaiah University of Applied Sciences. Her research interests include machine learning, data analytics, statistical and predictive analytics, healthcare analytics, computer vision, deep learning and image processing. Email: [email protected]

Chithra Priya Srinivasan

Chithra Priya Srinivasan received her BE degree in CSE from Dr Ambedkar Institute of Technology, and currently pursuing her MTech in machine learning and intelligent systems from MS Ramaiah University of Applied Sciences, Bengaluru, India. Her research interests include machine learning, data science, artificial intelligence and medical related fields. Email: [email protected]

N. Swathi

N Swathi received her BE degree in EEE from University Visvesvaraya College of Engineering, MTech in MLIS from M S Ramaiah University of applied sciences, Bangalore, India. Her research interests include computer vision, healthcare analytics, image processing and machine learning. Email: [email protected]

S. Sushma

S Sushma received her BE degree in ECE from Sri Venkateshwara College of Engineering and currently pursuing her MTech in machine learning and intelligent systems from M S Ramaiah University of Applied Sciences, Bengaluru, India. Her research interests include machine learning, deep learning algorithms, image processing. Email: [email protected]

Nitin Bhushan K. N.

Nitin Bhushan K N received his BE degree in ECE from T John Institute of Technology and currently pursuing his MTech in computer science and networking from M S Ramaiah University of applied sciences, Bengaluru, India. His research interests include data mining and analytics, cloud computing, machine learning, enterprise computing, image processing. Email: [email protected]

C. Narendra Babu

C Narendra Babu received his BE degree in CSE from Adichunchanagiri Institute of Technology, MTech degree in CSE from M S Ramaiah Institute of Technology and the PhD degree from Jawaharlal Nehru Technological University Anantapur. He is currently an associate professor with Department of Computer Science and Engineering, M S Ramaiah University of Applied Sciences, Bangalore. His research interests include data analytics, social media analytics, time series and spatio-temporal data modeling and machine learning.