{'fraction_non_alphanumeric': 0.022885572139303482, 'fraction_numerical': 0.001990049751243781, 'mean_word_length': 5.096969696969697, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
ASK: A Framework for Data Acquisition and Activity Recognition

This work puts forward a framework for the acquisition and processing of biosignals to indicate strain on the knee inflicted by human everyday activities. Such a framework involves the appropriate equipment in devices and sensors to capture factors that inflict strain on the knee, the long-term recording and archiving of corresponding multi-sensory biosignal data, the semi-automatic annotation and segmentation of these data, and the person-dependent or person-adaptive automatic recognition of strain. In this paper we present first steps toward our goal, i.e. person-dependent recognition of a small set of human everyday activities. The focus here is on the fully automatic end-to-end processing from signal input to recognition output. The framework was applied to collect and process a small pilot dataset from one person for a proof-of-concept validation and achieved 97% accuracy in recognizing instances of seven daily activities.