wearable sensors

Pitts MN, Ebers MR, Agresta CE, Steele KM (2025) Evaluating Sparse Inertial Measurement Unit Configurations for Inferring Treadmill Running Motion

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Journal Article in Sensors

 Inertial measurement units (IMUs) are used to analyze running performance. While leveraging one sensor to estimate kinematic and kinetic variables is common, sparsity limits the number of digital biomarkers that can be evaluated.

An illustration demonstrating experimental factors influencing the accuracy of motion inference when using Shallow recurrent decoder networks (SHRED) can reconstruct a dense set of time-series signals from a single input sensor. Running speed and sampling rate were most influential, while sensor location, and sensor type were neutral.Aim: Shallow recurrent decoder networks (SHRED) can reconstruct a dense set of time-series signals from a single input sensor and have been successful in human mobility applications, highlighting the potential for this algorithm to monitor running.

Methods: We trained and tested subject-specific SHRED models of nine subjects running on a treadmill to map from one input sensor to the remaining three IMUs. We varied the type of input to reflect experimental parameters that are important in running studies—sensor location, sensor type, sampling rate, and running speed—and compared the error of inferred signals from each input type.

Results: Sensor location and type did not impact SHRED inference accuracy, while decreasing the sampling rate affected the accuracy of ankle measurements. All ankle acceleration inferences from these models remained below the minimal detectable change threshold of 12.0 m/s2. SHRED models trained and tested at multiple speeds did not accurately infer IMU measurements below this threshold.

Interpretation: SHRED may broaden the scope of motion analysis by expanding access to data with fewer sensors. The data from this study and an instructional Jupyter notebook for training and testing individualized SHRED models are available at [link to GitHub].

CMBBE 2024 Recap

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Members of the Steele Lab traveled to Vancouver, BC for the 19th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering (CMBBE) hosted by the University of British Columbia.

PhD candidate, Mackenzie Pitts, gave a poster presentation on “Inferring Unmeasured Inertial Data from Sparse Sensing for Treadmill Running”. Steele Lab Alumni and Post-Doctoral Research Fellow at Emory University, Michael Rosenberg, gave a podium presentation titled “Recurrent Neural Network Gait Signatures Encode Speed-Induced Changes in Post-Stroke Gait Quality.”

In addition to sharing their research at the conference, the Steele Lab enjoyed connecting with fellow biomechanics and biomedical engineering researchers as well as exploring the beautiful campus at UBC.

MR Ebers, JP Williams, KM Steele, JN Kutz (2024) “Leveraging arbitrary mobile sensor trajectories with shallow recurrent decoder networks for full-state reconstruction,”

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Journal Article in IEEE Access

Sensing is a fundamental task for the monitoring, forecasting, and control of complex systems. In many applications, a limited number of sensors are available and must move with the dynamics. Currently, optimal path planning, like Kalman filter estimation, is required to enable sparse mobile sensing for state estimation. However, we show that arbitrary mobile sensor trajectories can be used. By adapting the Shallow REcurrent Decoder (SHRED) network with mobile sensors, their time-history can be used to encode global information of the measured high-dimensional state space.

Summary figure of a shallow recurrent decoder network (SHRED) leveraging mobile sensors to reconstruct full state-space estimates from sparse dynamical trajectories. (Left) Sensor trajectory history encodes global information of the spatio-temporal dynamics of the sparsely measured system. In this work, we evaluate three challenging datasets, including forced isotropic turbulence, global sea-surface temperature, and human biomechanics. (Middle) The mobile SHRED architecture can (i) embed the multiscale physics of a system into a compact and low-dimensional latent space, and (ii) provide a mapping from the sparse mobile sensors to a full state estimate. (Right) The high-dimensional and complex system states can be reconstructed, provided training data for the dynamical trajectory of the sensor(s) is available.Aim: We leverage sparse mobile sensor trajectories for full-state estimation, agnostic to sensor path.

Methods: Using modern deep learning architectures, we show that a sequence-to-vector model, such as an LSTM (long, short-term memory) network, with a decoder network, dynamic trajectory information can be mapped to full state-space estimates.

Results: We demonstrate that by leveraging mobile sensor trajectories with shallow recurrent decoder networks, we can train the network (i) to accurately reconstruct the full state space using arbitrary dynamical trajectories of the sensors, (ii) the architecture reduces the variance of the mean-squared error of the reconstruction error in comparison with immobile sensors, and (iii) the architecture also allows for rapid generalization (parameterization of dynamics) for data outside the training set. Moreover, the path of the sensor can be chosen arbitrarily, provided training data for the spatial trajectory of the sensor is available.

Interpretation: The time-history of mobile sensors can be used to encode global information of the measured high-dimensional state space.

Megan Ebers Presents at 2024 WiDS Puget Sound Conference

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On May 14, 2024, Steele Lab members Dr. Megan Ebers, Mackenzie Pitts, and Dr. Kat Steele attended the Women in Data Science (WiDS) Puget Sound conference hosted at Seattle University. WiDS aims to inspire and educate data scientists worldwide, regardless of gender, and to support women in the field.

Among the speakers at the conference, postdoctoral scholar Dr. Megan Ebers gave a presentation titled “Data Expansion to Improve Accuracy and Availability of Digital Biomarkers for Human Health and Performance.”

A professional woman standing confidently in front of a projector screen, delivering a presentation.

UW Data Science Seminar with Megan Ebers

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Title slide from the UW eScience Data Science seminar that says "Mobile sensing with shallow recurrent decoder networks. Megan R. Ebers"

Steele lab member and postdoctoral scholar, Megan Ebers, was featured in the Winter 2024 UW Data Science Seminar series. You can watch her full presentation on “Mobile sensing with shallow recurrent decoder networks” linked HERE on UW eScience Institute’s YouTube channel.

Abstract: Sensing is a fundamental task for the monitoring, forecasting, and control of complex systems. In many applications, a limited number of sensors are available and must move with the dynamics, such as with wearable technology or ocean monitoring buoys. In these dynamic systems, the sensors’ time history encodes a significant amount of information that can be extracted for critical tasks. We show that by leveraging the time-history of a sparse set of sensors, we can encode global information of the measured high-dimensional system using shallow recurrent decoder networks. This paradigm has important applications for technical challenges in climate modeling, natural disaster evaluation, and personalized health monitoring; we focus especially on how this paradigm has the potential to transform the way we monitor and manage movement-related health outcomes.

Bio: Megan Ebers is a postdoctoral scholar in applied mathematics with UW’s NSF AI Institute in Dynamic Systems. In her PhD research, she developed and applied machine learning methods for dynamics systems to understand and enable human mobility. Her postdoctoral research focuses on data-driven and reduced-order methods for complex systems, so as to continue her work in human-centered research challenges, as well as to extend her research to a broader set of technical challenges, including turbulent flow modeling, natural disaster monitoring, and acoustic object detection.