├── .gitignore ├── Human_Activity_Detection-1D CNN.ipynb ├── Human_Activity_Detection-ANN.ipynb ├── LICENSE ├── README.md ├── dataset ├── WISDM_ar_latest.tar.gz ├── WISDM_ar_v1.1_raw_about.txt ├── WISDM_ar_v1.1_trans_about.txt ├── WISDM_ar_v1.1_transformed.arff └── readme.txt ├── pics ├── 1.png ├── 2.jpg ├── 2.png └── 3.jpg └── sensorKDD-2010.pdf /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Activity-Detection-using-IMU-sensor 2 | 3 | 4 | 5 | User activity detection using IMU (Inertial Measurement Unit) sensors and power of deep learning. The accelerometer data from smart wearables is used for continuous activity detection, which can be used for in depth activity monitoring and recommender systems. 6 | 7 | The data set that I am using is a collection of accelerometer data taken from a smartphone that various people carried with them while conducting six different exercises (Downstairs, Jogging, Sitting, Standing, Upstairs, Walking). For each exercise the acceleration for the x, y, and z axis was measured and captured with a timestamp and person ID. 8 | 9 | With this available data, we would like to train a neural network in order to understand if a person carrying a smartphone is performing any of the six activities. Once the neural network has been trained on the existing data, it should be able to correctly predict the type of activity a person is conducting when given previously unseen data. 10 | 11 | Based on the available data the DL model will learn how to differentiate between each of the six activities. We can then show new data to the neural network and it will tell us what the user is doing at any particular point in time. 12 | 13 | 14 | -------------------------------------------------------------------------------- /dataset/WISDM_ar_latest.tar.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jidhu-mohan/Activity-Detection-using-IMU-sensor/4cd4db51027aeb4c93ae0cc9fbf1eb118cc0853c/dataset/WISDM_ar_latest.tar.gz -------------------------------------------------------------------------------- /dataset/WISDM_ar_v1.1_raw_about.txt: -------------------------------------------------------------------------------- 1 | raw_about.txt for WISDM_Act_v1.1 dataset 2 | 3 | See readme.txt for information about the WISDM Lab, rights, 4 | and other general information. 5 | 6 | Associated tasks: classification 7 | Number of examples: 1,098,207 8 | Number of attributes: 6 9 | Missing attribute values: None 10 | Class distribution: { 11 | Walking -> 424,400 -> 38.6%, 12 | Jogging -> 342,177 -> 31.2%, 13 | Upstairs -> 122,869 -> 11.2%, 14 | Downstairs -> 100,427 -> 9.1%, 15 | Sitting -> 59,939 -> 5.5%, 16 | Standing -> 48,395 -> 4.4% } 17 | 18 | raw.txt follows this format: 19 | [user],[activity],[timestamp],[x-acceleration],[y-accel],[z-accel]; 20 | 21 | This line is a representative example: 22 | 33,Jogging,49105962326000,-0.6946377,12.680544,0.50395286; 23 | 24 | Sampling rate: 25 | 20Hz (1 sample every 50ms) 26 | 27 | Fields: 28 | *user 29 | nominal, 1..36 30 | 31 | *activity 32 | nominal, { 33 | Walking 34 | Jogging 35 | Sitting 36 | Standing 37 | Upstairs 38 | Downstairs } 39 | 40 | *timestamp 41 | numeric, generally the phone's uptime in nanoseconds 42 | (In future datasets this will be miliseconds 43 | since unix epoch.) 44 | 45 | *x-acceleration 46 | numeric, floating-point values between -20 .. 20 47 | The acceleration in the x direction as measured 48 | by the android phone's accelerometer. 49 | A value of 10 = 1g = 9.81 m/s^2, and 50 | 0 = no acceleration. 51 | The acceleration recorded includes gravitational 52 | acceleration toward the center of the Earth, so 53 | that when the phone is at rest on a flat surface 54 | the vertical axis will register +-10. 55 | 56 | *y-accel 57 | numeric, see x-acceleration 58 | 59 | *z-accel 60 | numeric, see x-acceleration 61 | -------------------------------------------------------------------------------- /dataset/WISDM_ar_v1.1_trans_about.txt: -------------------------------------------------------------------------------- 1 | trans_about.txt for WISDM_Act_v1.1 dataset 2 | 3 | See readme.txt for information about the WISDM Lab, rights, 4 | and other general information. 5 | 6 | Associated tasks: classification 7 | Number of examples: 5,424 8 | Number of attributes: 46 9 | Missing attribute values: None 10 | Class distribution: { 11 | Walking -> 2,082 -> 38.4%, 12 | Jogging -> 1,626 -> 30.0%, 13 | Upstairs -> 633 -> 11.7%, 14 | Downstairs -> 529 -> 9.8%, 15 | Sitting -> 307 -> 5.7%, 16 | Standing -> 247 -> 4.6% } 17 | 18 | 19 | transformed.arff follows the Attribute-Relation File Format 20 | specified here: 21 | 22 | 23 | 24 | For our transformation process, we take 10 seconds worth of 25 | accelerometer samples (200 records/lines in the raw file) 26 | and transform them into a single example/tuple of 46 values. 27 | Most of the features we generate are simple statistical 28 | measures. 29 | 30 | 31 | Field descriptions: 32 | (To see the field definitions, read the arff file's header.) 33 | For a detailed specification, see section 2.2 of: 34 | Jennifer R. Kwapisz, Gary M. Weiss and Samuel A. Moore (2010). 35 | "Activity Recognition using Cell Phone Accelerometers," 36 | Proceedings of the Fourth International Workshop on 37 | Knowledge Discovery from Sensor Data (at KDD-10), 38 | Washington DC. 39 | 40 | 41 | UNIQUE_ID is just that, a unique identifier for each tuple. 42 | We exclude this field when making predictions 43 | 44 | user is the id number of the userthat the data is from. 45 | 46 | X0..x9, Y0..Y9, Z0..Z9 are bins, their values are the fraction 47 | of accelerometer samples that fell within that bin 48 | 49 | XAVG, YAVG, ZAVG are the average x, y, and z values over the 50 | 200 records in the example. 51 | 52 | XPEAK, YPEAK, ZPEAK are approximations of the dominant 53 | frequency. First, the greatest value in the series is 54 | identified, then all local peak values within 10% of 55 | its amplitude are identified. If the number of peaks 56 | is less than 3, then the threshhold is lowered until 57 | at least 3 peaks can be found. The times between 58 | consecutive peaks are summed and divided by the number 59 | of peaks. 60 | 61 | XABSOLDEV, YABSOLDEV, ZABSOLDEV are the average absolute 62 | deviations from the mean value for each axis. 63 | 64 | XSTANDDEV, YSTANDDEV, ZSTANDDEV are the standard deviations 65 | for each axis. 66 | 67 | RESULTANT is the average of the square roots of the sum of the values 68 | of each axis squared √(xi^2 + yi^2 + zi^2). 69 | 70 | class is the activity that the user was performing during this example. 71 | -------------------------------------------------------------------------------- /dataset/readme.txt: -------------------------------------------------------------------------------- 1 | Readme file for WISDM's activity prediction dataset v1.1 2 | Updated: Dec. 2, 2012 3 | 4 | This data has been released by the Wireless Sensor Data Mining 5 | (WISDM) Lab. 6 | 7 | The data in this file corrispond with the data used in the 8 | following paper: 9 | 10 | Jennifer R. Kwapisz, Gary M. Weiss and Samuel A. Moore (2010). 11 | Activity Recognition using Cell Phone Accelerometers, 12 | Proceedings of the Fourth International Workshop on Knowledge 13 | Discovery from Sensor Data (at KDD-10), Washington DC. 14 | 15 | 16 | When using this dataset, we request that you cite this paper. 17 | 18 | You may also want to cite our other relevant articles, which 19 | can be found here: 20 | 21 | 22 | Jeffrey W. Lockhart, Tony Pulickal, and Gary M. Weiss (2012). 23 | "Applications of Mobile Activity Recognition," 24 | Proceedings of the ACM UbiComp International Workshop 25 | on Situation, Activity, and Goal Awareness, Pittsburgh, 26 | PA. 27 | 28 | Gary M. Weiss and Jeffrey W. Lockhart (2012). "The Impact of 29 | Personalization on Smartphone-Based Activity Recognition," 30 | Proceedings of the AAAI-12 Workshop on Activity Context 31 | Representation: Techniques and Languages, Toronto, CA. 32 | 33 | Jennifer R. Kwapisz, Gary M. Weiss and Samuel A. Moore (2010). 34 | "Activity Recognition using Cell Phone Accelerometers," 35 | Proceedings of the Fourth International Workshop on 36 | Knowledge Discovery from Sensor Data (at KDD-10), Washington 37 | DC. 38 | 39 | When sharing or redistributing this dataset, we request that 40 | this readme.txt file is always included. 41 | 42 | Files: 43 | readme.txt 44 | WISDM_ar_v1.1_raw_about.txt 45 | WISDM_ar_v1.1_trans_about.txt 46 | WISDM_ar_v1.1_raw.txt 47 | WISDM_ar_v1.1_transformed.arff 48 | 49 | Changelog (v1.1): 50 | * about files updated with summary information 51 | * file naming convention updated to include version numbers 52 | * readme.txt updated to include relevant papers 53 | * WISDM_ar_v1.1_trans_about.txt updated with citation to paper 54 | describing the attributes. 55 | 56 | Changelog (v1.0): 57 | * user names masked with ID numbers 1-36 58 | * dataset initialized 59 | -------------------------------------------------------------------------------- /pics/1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jidhu-mohan/Activity-Detection-using-IMU-sensor/4cd4db51027aeb4c93ae0cc9fbf1eb118cc0853c/pics/1.png -------------------------------------------------------------------------------- /pics/2.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jidhu-mohan/Activity-Detection-using-IMU-sensor/4cd4db51027aeb4c93ae0cc9fbf1eb118cc0853c/pics/2.jpg -------------------------------------------------------------------------------- /pics/2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jidhu-mohan/Activity-Detection-using-IMU-sensor/4cd4db51027aeb4c93ae0cc9fbf1eb118cc0853c/pics/2.png -------------------------------------------------------------------------------- /pics/3.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jidhu-mohan/Activity-Detection-using-IMU-sensor/4cd4db51027aeb4c93ae0cc9fbf1eb118cc0853c/pics/3.jpg -------------------------------------------------------------------------------- /sensorKDD-2010.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jidhu-mohan/Activity-Detection-using-IMU-sensor/4cd4db51027aeb4c93ae0cc9fbf1eb118cc0853c/sensorKDD-2010.pdf --------------------------------------------------------------------------------