In the last decade, Human Activity Recognition (HAR) has become a vibrant research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present in our daily lives. In addition, the advance of deep learning and other machine learning algorithms has allowed researchers to use HAR in various domains including sports, health and well-being applications. For example, HAR is considered as one of the most promising assistive technology tools to support elderly's daily life by monitoring their cognitive and physical function through daily activities. This survey focuses on critical role of machine learning in developing HAR applications based on inertial sensors in conjunction with physiological and environmental sensors.INDEX TERMS Human activity recognition (HAR), deep learning (DL), machine learning (ML), available datasets, sensors, accelerometer.
Transaction-level modeling (TLM) has been proposed as the leading strategy to address the always increasing complexity of digital systems. However, its introduction arouses a new challenge for designers and verification engineers, since there are no mature tools to automatically synthesize an RTL implementation from a transaction-level (TL) design, thus manual refinements are mandatory. In this context, the paper presents an incremental assertionbased verification (ABV) methodology to check the correctness of the TL-to-RTL refinement. The methodology relies on reusing assertions and already checked code, and it is guided by an assertion coverage metrics.
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