Abstract
Wearable sensor-based systems and devices have been expanded in different application domains, especially in the healthcare arena. Automatic age and gender estimation has several important applications. Gait has been demonstrated as a profound motion cue for various applications. A gait-based age and gender estimation challenge was launched in the 12th IAPR International Conference on Biometrics (ICB), 2019. In this competition, 18 teams initially registered from 14 countries. The goal of this challenge was to find some smart approaches to deal with age and gender estimation from sensor-based gait data. For this purpose, we employed a large wearable sensor-based gait dataset, which has 745 subjects (357 females and 388 males), from 2 to 78 years old in the training dataset; and 58 subjects (19 females and 39 males) in the test dataset. It has several walking patterns. The gait data sequences were collected from three IMUZ sensors, which were placed on waist-belt or at the top of a backpack. There were 67 solutions from ten teams—for age and gender estimation. This paper extensively analyzes the methods and achieved-results from various approaches. Based on analysis, we found that deep learning-based solutions lead the competitions compared with conventional handcrafted methods. We found that the best result achieved 24.23% prediction error for gender estimation, and 5.39 mean absolute error for age estimation by employing angle embedded gait dynamic image and temporal convolution network.
Highlights
The number of elderly people is increasing globally, especially in Japan, South Korea, New Zealand, and some European countries
Apart from these, we have a good number of algorithms tested on this large dataset—so that we can get more insights and methods that are more suitable for age and gender estimations based on sensor-based gait data
We rigorously analyzed the experimental results for age prediction and gender estimation
Summary
The number of elderly people is increasing globally, especially in Japan, South Korea, New Zealand, and some European countries. Estimations of age and gender are not much explored using wearable sensors [9,15]. We highlight the importance of gait, summarize various approaches related to gait, and age and gender estimations. A shorter version of this challenge was published in [16] In this feature article, we rigorously enhance the previous paper [16] with more extended presentations, new related work (there was no related work in the earlier version), enhanced descriptions of datasets, enriched evaluation methods and features (enhanced by more than 12 times, in terms of texts), and detailed results and analysis. This version of the paper has highly engraved enriched information, analysis, and discussions
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