The vision of embedded intelligence, that computing should be indistinguishable from everyday objects. Introduction of activity recognition. The categories and hierarchy of activity contexts. The ethical topic in artificial intelligence.

Location awareness is the fundemental context in embedded intelligence, including topics from localization to locomotion tracking. We will introduce the basic localization principles, satellite locationing systems, indoor localization techniques, and location context models.

Activity and gesture recognition using IMUs. Introducing machine learning workflow, time signal processing, frequency analysis with fast fourier transform and wavelet transform, and result presentation with confusion matrix.

Ambient activity recognition with multiple environment sensors. Introducing sensor fusion, classifier basics, overfitting and spotting.

Monitoring physiological signals with embedded sensing systems. Indepth look at using surface pressure mechanomyography to detect various muscle activity patterns, including introduction to dynamic time warping.

Introduction to neural networks and deep learning in the context of activity recognition. Explains dense and CNN layers. Further discussion on sensor fusion and neural networks with the RBK dataset study.

Knowledge domain transfer utilizes part of models trained with other knowledge to detect different domains. Further introduction on RNN units. Examples with image networks in pressure mapping imagery recognition, and time series recognition with spectrogram.

Affective computing and psycho-informatics. Examples of bipolar detection using smartphone methods, expression and cognitive load detection using Expressure, and affective touch for robot skin.

Optimize the computing and power efficiency for embedded sensing and activity recognition systems. Detailed analysis on feature selection and reduction using PCA, LDA and NCA methods.