Skip to content

Latest commit

 

History

History
166 lines (134 loc) · 6.47 KB

README.md

File metadata and controls

166 lines (134 loc) · 6.47 KB

Human Activity Detection Using Phone and Watch Sensors

Dataset

WISDM Smartphone and Smartwatch Activity and Biometrics Dataset

Dataset Summary

Metric Value
Number of subjects 51
Number of activities 18
Minutes collected per activity 3
Sensorspolling rate 20Hz
Smartphone used Google Nexus 5/5X or Samsung Galaxy S5
Smartwatch used LG G Watch
Number of raw measurements 15,630,426

18 activity are recorded and coded as follow

Activity Code
Walking A
Jogging B
Stairs C
Sitting D
Standing E
Typing F
Brushing Teeth G
Eating Soup H
Eating Chips I
Eating Pasta J
Drinking from Cup K
Eating Sandwich L
Kicking (Soccer Ball) M
Playing Catch w/Tennis Ball O
Dribbling (Basketball) P
Writing Q
Clapping R
Folding Clothes S

Prerequisites :

python >= 3.8

pip install -r requirements.txt

Data Preprocessing

code/001_data_collection_preprocessing_phone_data.ipynb
code/001_data_collection_preprocessing_watch_data.ipynb

EDA

code/002_eda_phone_data.ipynb
code/002_eda_watch_data.ipynb

Feature extraction

Performs signal processing and generates processed dataset in dataset/ folder

code/003_feature_extraction.ipynb

Training

Note: It is prerequisite to execute above feature extraction since Logistic regression, Random forest and KNN works on feature extracted dataset.

1. Logistic regression

code/004_1_train_logisticregression_phone.ipynb
code/004_1_train_logisticregression_watch.ipynb

2. KNN

code/004_3_train_knn_phone.ipynb
code/004_3_train_knn_watch.ipynb

3. Random forest

code/004_2_train_randomforest_phone.ipynb
code/004_2_train_randomforest_watch.ipynb
code/004_2_train_randomforest.ipynb

4. LSTM

code/004_4_train_lstm_phone.ipynb
code/004_4_train_lstm_watch.ipynb
code/004_4_train_lstm.ipynb

5. GRU

code/004_5_train_gru_phone.ipynb
code/004_5_train_gru_watch.ipynb
code/004_5_train_gru.ipynb

Results

Single Sensor

1. Logistic regression

Sensors Accuracy(%)
Phone Accelerometer 39.57
Phone Gyroscope 40.56
Watch Accelerometer 73.06
Watch Gyroscope 65.08

2. KNN

Sensors Accuracy(%)
Phone Accelerometer 59.29
Phone Gyroscope 44.20
Watch Accelerometer 62.64
Watch Gyroscope 57.03

3. Random forest

Sensors Accuracy(%)
Phone Accelerometer 72.99
Phone Gyroscope 63.81
Watch Accelerometer 85.60
Watch Gyroscope 76.60

4. LSTM

Sensors Accuracy(%)
Phone Accelerometer 25.75
Phone Gyroscope 49.37
Watch Accelerometer 74.97
Watch Gyroscope 76.57

5. GRU

Sensors Accuracy(%)
Phone Accelerometer 30.10
Phone Gyroscope 53.03
Watch Accelerometer 78.38
Watch Gyroscope 80.80

Multi Sensors

1. Random forest

Sensors Accuracy(%)
Phone Accel & Gyro 81.04
Watch Accel & Gyro 90.26
All 4 Sensors 93.17
All 4 Sensors (15 activities)¹ 96.12

2. LSTM

Sensors Accuracy(%)
Phone Accel & Gyro 52.79
Watch Accel & Gyro 85.19
All 4 Sensors 85.11
All 4 Sensors (15 activities)¹ 89.32

3. GRU

Sensors Accuracy(%)
Phone Accel & Gyro 58.18
Watch Accel & Gyro 88.14
All 4 Sensors 88.06
All 4 Sensors (15 activities)¹ 92.27

¹ dropping "eating chips", "eating pasta", "eating sandwich" activities.

Conclusion

This project focused on analysing the WISDM dataset to classify human activities using a range of machine learning and deep learning models. Among the tested approaches, the Random Forest classifier stood out as the top performer, delivering the highest accuracy and computational efficiency. Its robust handling of feature importance and generalization across the dataset made it a reliable choice for this task.

Deep learning models, particularly LSTM and GRU, also showed strong potential. These models worked well in capturing temporal patterns and dependencies inherent in sequential data, showing their suitability for activity recognition tasks. However, further optimization through hyperparameter tuning and architectural adjustments could enhance their performance and fully leverage their strengths.

Future directions for this work could include integrating ensemble techniques to combine the strengths of different models, developing hybrid frameworks that utilize both traditional machine learning and deep learning, and employing more advanced preprocessing steps to improve data quality. These efforts could drive better performance, greater adaptability and deeper insights into human activity classification.

Code Contributors

        1) Murthy L
        2) Moushumi Mahato
        3) Shashankgoud Patil