{'fraction_non_alphanumeric': 0.016151685393258428, 'fraction_numerical': 0.0, 'mean_word_length': 5.390134529147982, '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}}
The MobiFall dataset: An initial evaluation of fall detection algorithms using smartphones

Fall detection receives significant attention in the field of preventive medicine, wellness provision and assisted living, especially for the elderly. As a result, numerous commercial fall detection systems exist to date and most of them use accelerometers and/ or gyroscopes attached on a person's body as primary signal sources. These systems use either discrete sensors as part of a product designed specifically for this task or sensors that are embedded in mobile devices such as smartphones. The latter approach has the advantage of offering well tested and widely available communication services, e.g. for calling emergency if necessary, when someone has fallen. Apparently, automatic fall detection will continue to evolve in the following years. The aim of this work is to introduce a human activity dataset that will be helpful in testing new methods, as well as performing objective comparisons between different algorithms for fall detection and activity recognition, based on inertial-sensor data from smartphones. The dataset contains signals recorded from the accelerometer and gyroscope sensors of a latest technology smartphone for four different falls and nine different activities of daily living. Using this dataset, the results of an initial evaluation of three fall detection algorithms are finally presented.