Recognizing Human Activities from Smartphone Sensors

Smartphones are equipped with powerful sensors like accelerometers and gyroscopes that continuously capture human motion data. Human Activity Recognition (HAR) uses this data to identify activities like walking, sitting, running, cycling, or climbing stairs. Accurate activity recognition has critical applications in healthcare, fitness tracking, elderly monitoring, and smart assistants. The challenge lies in capturing subtle differences between activities and building models that can generalize across users and environments.

By collecting time-series sensor data from smartphones, machine learning and deep learning models like Random Forests, CNNs, and LSTMs can classify user activities in real-time. Feature extraction techniques, like computing statistical features over sliding windows, boost model accuracy. These activity recognition systems enable smarter apps for fitness, healthcare monitoring, and human-computer interaction.

Sensor data from accelerometers and gyroscopes is collected at regular intervals, forming multivariate time series. Statistical features (mean, variance, entropy, FFT coefficients) are extracted over sliding windows. Machine learning classifiers or deep sequence models are then trained to recognize different activity patterns. Real-time HAR systems use continuous sensor input to classify user activities instantly, supporting a wide range of smart health and fitness applications.

• Collect labeled accelerometer and gyroscope data using smartphone sensors during different activities (e.g., walking, running, sitting).
• Preprocess sensor data by smoothing signals, segmenting time windows, and extracting statistical and frequency-based features.
• Train machine learning models like Random Forest, SVM, or deep learning models like CNNs, LSTMs to classify activities.
• Evaluate models using accuracy, precision, recall, F1-score, and confusion matrices across various activity classes.
• Deploy a real-time activity recognition system that takes sensor streams and predicts current user activity dynamically.

• UCI Human Activity Recognition Dataset
• WISDM Smartphone Activity Recognition Dataset
• Human Activity Recognition with Deep Learning