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Exercise Routine Optimization Via Sensor Fusion Farib Khondoker, REU Student

Graduate Mentor: Uday Shantamallu, Payam Mehr Faculty Advisor: Drs. Trevor Thornton and Andreas Spanias

MOTIVATION

Demonstrate the effectiveness of data-driven predictive analytics

Utilize easily-accessible sensor board for consumer applications

Develop a model for classifying/optimizing workout routines

PROBLEM STATEMENT

Extreme workout routines cause excessive stress on muscles, tendons, etc.

Method required to record previous workout data, and optimize based on workout, needs, etc.

METHOD

1. Acquire Data via Sensor Platform

Sensors and Dev. Board: NXP Freedom K66F, TI CC3200STK SensorTag

2. Define/Extract Features from Data

Time domain: Translational/Rotational Motion in XYZ Directions

PCA – Reduce dimensionality of dataset

(0=stairs, 1=stationary, 2=walking, 3=fast-walking)

TRAINING SET RESULTS

3. Classify Data via Fine Gaussian SVM (Supervised Algorithm)

Supervised Learning labeled features

~87% accuracy for four-state model

(0=stairs, 1=stationary, 2=walking, 3=fast-walking)

Sensor Signal and Information Processing Center http://sensip.asu.edu

SenSIP Center, School of ECEE, Arizona State University

SenSIP Algorithms and Devices REU

ABSTRACT

REFERENCES [1] I. Pernek et al, "Recognizing the intensity of strength training exercises with wearable sensors," Journal of Biomedical Informatics, vol. 58, pp. 145-155, 2015. [2] M. O’Reilly et al, "Technology in S&C: Tracking Lower Limb Exercises with Wearable Sensors," Journal of Strength and Conditioning Research, pp. 1, 2017. [3] Y. Qi et al, "Lower Extremity Joint Angle Tracking with Wireless Ultrasonic Sensors during a Squat Exercise," Sensors (Basel, Switzerland), vol. 15, pp. 9610-9627, 2015. [4] B. Zhou et al, "Measuring muscle activities during gym exercises with textile pressure mapping sensors," Pervasive and Mobile Computing, 2016. [5] C. Liu and C. Chan, "Exercise Performance Measurement with Smartphone Embedded Sensor for Well-Being Management," International Journal of Environmental Research and Public Health, vol. 13, pp. 1001, 2016. [6]"The Importance of Physical Activities and How to Achieve It", CA Physical Activity, 2017. [Online]. Available: http://www.caphysicalactivity.org/. [Accessed: 14- Jun- 2017]. [7]"FRDM-K64F|Freedom Development Platform|Kinetis MCUs|NXP", Nxp.com, 2017. . [8]S. Dirk, "[Big] Data Management", Ignite, 2017. [Online]. Available: http://enterprise-iot.org/book/enterprise-iot/part-ii-igniteiot-methodology/igniteiot-solution-delivery/building-blocks/iot-technology-profiles/4-middleware/scenario-a-data-management-for-basic-m2m/. [Accessed: 15- Jun- 2017]. [9]C. Piech, "CS221", Stanford.edu, 2017. [Online]. Available: http://stanford.edu/~cpiech/cs221/handouts/kmeans.html. [Accessed: 15- Jun- 2017]. [10] L. Yang et al, "Finger vein image quality evaluation using support vector machines,"Optical Engineering, vol. 52, (2), pp. 27003, 2013.

This material is based upon work supported by the National Science Foundation under Grant No. CNS 1659871 REU Site: Sensors, Signal and Information Processing Devices and Algorithms.

Extreme workout routines may cause irreparable damage to body.

Modern machine learning algorithms can minimize workout severity and maximize workout gains.

We will develop methods to utilize integrated sensor data and machine learning techniques to create an ideal workout routine.

FUTURE DEVELOPMENTS

Can be extended to other applications such as:

Rehabilitation

Disease-specific Health Management

Environmental and Safety Conditions