ECE 539 Project Proposal Marc Petit

Human Activity Recognition Using Smartphone Sensor Data

The objective of this project is to use gyroscope and accelerometer sensor data from a cellphone

to recognize the current user activity (walking, sitting, standing, walking upstairs, walking

downstairs, and laying).

For this project an existing data set from the UCI Machine Learning Repository is used [1]. The

experiments have been carried out with a group of 30 volunteers within an age bracket of

19-48 years. Each person performed six activities (walking, walking upstairs, walking downstairs,

sitting, standing, laying) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its

embedded accelerometer and gyroscope, 3-axial linear acceleration and 3-axial angular velocity

at a constant rate of 50 Hz were captured. [2]

In figure 1 example data of one of the 61 experiments is shown. In each experiment 22 activities

are performed and the time frames for each activity were extracted using videos of the

experiment. The dataset provides the activities and the time frames for each activity.

Additionally, using a 128 readings (2.56 sec) window the data has been sampled and

preprocessed. Using various time and frequency domain features such as mean, std. dev, etc. a

561 feature vector has been created.

Legend x-direction y-direction z-direction Settings User 1 Fsample = 50 Hz

Figure 1: Acceleration and velocity data for one out of the 61 conducted experiments. In this time frame 22 activities were performed. Two activities are marked as an example.

As a first step the given feature vector will be used to perform the following tasks:

1. Use different classifiers such a Nearest Neighbor and Bayesian Classifier to determine the

activity.

2. Using PCA, the feature vector should be reduced and it should be determined how many

and which features are necessary to keep the probability of misclassification under 10%.

standing

sitting

ECE 539 Project Proposal Marc Petit

In a second step the raw data will be used and the features will be extracted using a convolutional

neural network [3]. The results will be compared to the results achieved with the given feature

vector.

The main performance metric is the confusion matrix and the probability of misclassification. The

raw data is already provided as randomly separated test and training data. However, for this

project the given test and training data will be combined, randomized and separated again so

that three way cross-validation can be used.

The outcome of this project is a comparative evaluation of different methods to recognize the

human activity through cellphone sensor data using a given feature vector and using the raw data

with a convolutional neural network.

References

[1] UCI Machine Learning Repository, "Smartphone-Based Recognition of Human Activities and

Postural Transitions Data Set," 29 07 2015. [Online]. Available:

http://archive.ics.uci.edu/ml/datasets/Smartphone-

Based+Recognition+of+Human+Activities+and+Postural+Transitions. [Accessed 23 10

2017].

[2] J.-L. Reyes-Ortiz, L. Oneto, A. Samà, X. Parra and D. Anguita, "Transition-Aware Human

Activity Recognition Using Smartphones," Neurocomputing, pp. 754-767, 2016.

[3] M. Zeng, L. T. Nguyen, B. Yu, O. J. Mengshoel, J. Zhu, P. Wu and J. Zhang, "Convolutional

Neural Networks for Human Activity Recognition using Mobile Sensors," in International

Conference on Mobile Computing, Applications and Services (MobiCASE), 2014.