Activity-Aware Physiological Response Prediction Using Wearable Sensors

Abstract

Prediction of physiological responses can have a number of applications in the health and medical fields. However, this can be a challenging task due to interdependencies between these responses, physical activities, environmental factors and the individual's mental state. In this work, we focus on forecasting physiological responses in dynamic scenarios where individuals are performing exercises and complex activities of daily life. We minimize the effect of environmental and physiological factors in order to focus on the effect of physical activities. In particular, we focus on forecasting heart rate and respiratory rate due to their relevance in medical and fitness training. We aim to forecast these physiological responses up to 60 s into the future, study the effect of different predictors that incorporate different sensing modalities and different amounts of historical data and analyze the performance of various strategies for prediction. Activity information is incorporated by clustering the data streams and fitting different predictive models per cluster. The effect of clustering is also studied by performing a hierarchical analysis on the clustering parameter, and we observe that activity clustering does improve the performance in our proposed methodology when predicting physiological response across modalities.

Skeleton-Based Human Action Recognition through Third-Order Tensor Representation and Spatio-Temporal Analysis

“…1 37% of the MSRC-12 dataset was used for training and 63% for testing [32]. 2 Training and testing using "Leave-persons out" protocol [27].…”

“…Human action recognition has been an active research topic due to its wide range of applications, including surveillance, healthcare, safety, transportation, human-computer interactions and response prediction [1,2]. Furthermore, with the continuous development of cost-effective RGB (Red-Green-Blue) [3] and depth cameras [4], inertial sensors [5], and algorithms for real-time pose estimation, human action recognition receives growing attention nowadays.…”

“…Contribution of different feature representations and fusion results through discriminant correlation analysis for CERTH, G3D, and MSRC-12 databases. 1 37% of the MSRC-12 dataset was used for training and 63% for testing 2. "Leave-persons out" protocol was used.…”

Developing a novel force forecasting technique for early prediction of critical events in robotics

“…Exaggerated forecasts can be avoided if the above stability conditions are satisfied. However, underestimated (poor) forecasts still occur for a stable model depending on the values of F N (k) and λ from Eq (7). The worst case of an underestimated forecast isF N ðk þ 1Þ ¼F N ðkÞ, as shown in Fig 4. The next two forecast conditions state the range of PPD and λ required to prevent underestimated forecasts.…”

“…One critically important area for adverse event prediction is in the field of medicine. In the areas of medical decision making, time series forecasting models have been successfully applied to predict mortality and time dependent risks [5], early diagnosis of disease [6], and heartbeat rates to estimate activity-based physiological response [7]. However, there is a paucity of research related to prediction-based control for medical robotic systems, and in particular, surgical robots.…”