Activity2Vec: Learning ADL Embeddings from Sensor Data with a Sequence-to-Sequence Model
This addresses the need for efficient feature extraction in human health monitoring using sensor data, but it is incremental as it applies an existing seq2seq method to this domain.
The paper tackled the problem of automatically extracting universal features from sensor data for activity recognition and fall detection, proposing a sequence-to-sequence model that avoids feature engineering and enables semi-supervised learning, with evaluation on wearable and ambient sensor datasets.
Recognizing activities of daily living (ADLs) plays an essential role in analyzing human health and behavior. The widespread availability of sensors implanted in homes, smartphones, and smart watches have engendered collection of big datasets that reflect human behavior. To obtain a machine learning model based on these data,researchers have developed multiple feature extraction methods. In this study, we investigate a method for automatically extracting universal and meaningful features that are applicable across similar time series-based learning tasks such as activity recognition and fall detection. We propose creating a sequence-to-sequence (seq2seq) model to perform this feature learning. Beside avoiding feature engineering, the meaningful features learned by the seq2seq model can also be utilized for semi-supervised learning. We evaluate both of these benefits on datasets collected from wearable and ambient sensors.