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.

Forecasting physical functioning of people with Multiple Sclerosis (MS) can inform timely clinical interventions and accurate "day planning" to improve their well-being. However, people's physical functioning often remains unchecked in between infrequent clinical visits, leading to numerous negative healthcare outcomes. Existing Machine Learning (ML) models trained on in-situ data collected outside of clinical settings (e.g., in people's homes) predict which people are currently experiencing low functioning. However, they do not forecast if and when people's symptoms and behaviors will negatively impact their functioning in the future. Here, we present a computational behavior model that formalizes clinical knowledge about MS to forecast people's end-of-day physical functioning in advance to support timely interventions. Our model outperformed existing ML baselines in a series of quantitative validation experiments. We showed that our model captured clinical knowledge about MS using qualitative visual model exploration in different "what-if" scenarios. Our work enables future behavior-aware interfaces that deliver just-in-time clinical interventions and aid in "day planning" and "activity pacing".

Ensuring that Machine Learning (ML) models make correct and meaningful inferences is necessary for the broader adoption of such models into high-stakes decision-making scenarios. Thus, ML model engineers increasingly use eXplainable AI (XAI) tools to investigate the capabilities and limitations of their ML models before deployment. However, explaining sequential ML models, which make a series of decisions at each timestep, remains challenging. We present Visual Interactive Model Explorer (VIME), an XAI toolbox that enables ML model engineers to explain decisions of sequential models in different “what-if” scenarios. Our evaluation with 14 ML experts, who investigated two existing sequential ML models using VIME and a baseline XAI toolbox to explore “what-if” scenarios, showed that VIME made it easier to identify and explain instances when the models made wrong decisions compared to the baseline. Our work informs the design of future interactive XAI mechanisms for evaluating sequential ML-based decision support systems.