Author name cluster

Tian Tan

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

8 papers

1 author row

JBHI Journal 2026 Journal Article

Novel Deep Learning Model to Estimate Knee Flexion and Adduction Moments With Wearable IMUs During Treadmill and Overground Walking

Alon Sabaty
Adi Fishman
Shani Batcir
Tian Tan
Peter B. Shull
Kfir Levy
Arielle G. Fischer

A major issue after total knee replacement (TKR) surgery is asymmetric gait kinetics, which increases knee loads on the non-operated knee. This imbalance accelerates osteoarthritis (OA) progression, often leading to a second contralateral TKR. There is a clear need for an advanced wearable system with multiple sensors to accurately estimate gait kinetics in natural environments. This study aims to develop a machine learning framework that exclusively uses wearable inertial measurement units (IMUs) during overground and treadmill walking to estimate knee flexion moment (KFM) and knee adduction moment (KAM), significant biomechanical factors linked to OA. We introduce a novel deep learning model that combines a Long Short-Term Memory (LSTM)-based Autoencoder and Variational Gaussian Process (VGP) to estimate the mean and uncertainty region of the KAM and KFM. Seventeen healthy participants performed treadmill walking trials, while a separate group of seventeen healthy participants performed overground walking trials for model training and validation. Results demonstrated Root Mean Square Errors (RMSE) of 0. 49%BW $\cdot$ BH (body weight × body height) and 0. 73%BW $\cdot$ BH for KAM and KFM, respectively, during treadmill walking and 0. 74%BW $\cdot$ BH and 0. 49%BW $\cdot$ BH for KAM and KFM respectively during overground walking, which is more accurate than existing approaches. The proposed model with wearable IMUs could enable knee health monitoring and rehabilitation for these key biomechanical factors linked to the progression of knee joint pathologies outside of traditional biomechanical laboratories with large, tethered equipment and into clinics, hospitals, and the community.

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JBHI Journal 2025 Journal Article

Step Width Estimation in Individuals With and Without Neurodegenerative Disease via a Novel Data-Augmentation Deep Learning Model and Minimal Wearable Inertial Sensors

Hong Wang
Zakir Ullah
Eran Gazit
Marina Brozgol
Tian Tan
Jeffrey M. Hausdorff
Peter B. Shull
Penina Ponger

Step width is vital for gait stability, postural balance control, and fall risk reduction. However, estimating step width typically requires either fixed cameras or a full kinematic body suit of wearable inertial measurement units (IMUs), both of which are often too expensive and time-consuming for clinical application. We thus propose a novel data-augmented deep learning model for estimating step width in individuals with and without neurodegenerative disease using a minimal set of wearable IMUs. Twelve patients with neurodegenerative, clinically diagnosed Spinocerebellar ataxia type 3 (SCA3) performed over ground walking trials, and seventeen healthy individuals performed treadmill walking trials at various speeds and gait modifications while wearing IMUs on each shank and the pelvis. Results demonstrated step width mean absolute errors of 3. 3 $\pm$ 0. 7 cm and 2. 9 $\pm$ 0. 5 cm for the neurodegenerative and healthy groups, respectively, which were below the minimal clinically important difference of 6. 0 cm. Step width variability mean absolute errors were 1. 5 cm and 0. 8 cm for neurodegenerative and healthy groups, respectively. Data augmentation significantly improved accuracy performance in the neurodegenerative group, likely because they exhibited larger variations in walking kinematics as compared with healthy subjects. These results could enable clinically meaningful and accurate portable step width monitoring for individuals with and without neurodegenerative disease, potentially enhancing rehabilitative training, assessment, and dynamic balance control in clinical and real-life settings.

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AAAI Conference 2023 Conference Paper

Multi-Modality Deep Network for Extreme Learned Image Compression

Xuhao Jiang
Weimin Tan
Tian Tan
Bo Yan
Liquan Shen

Image-based single-modality compression learning approaches have demonstrated exceptionally powerful encoding and decoding capabilities in the past few years, but suffer from blur and severe semantics loss at extremely low bitrates. To address this issue, we propose a multimodal machine learning method for text-guided image compression, in which the semantic information of text is used as prior information to guide image compression for better compression performance. We fully study the role of text description in different components of the codec, and demonstrate its effectiveness. In addition, we adopt the image-text attention module and image-request complement module to better fuse image and text features, and propose an improved multimodal semantic-consistent loss to produce semantically complete reconstructions. Extensive experiments, including a user study, prove that our method can obtain visually pleasing results at extremely low bitrates, and achieves a comparable or even better performance than state-of-the-art methods, even though these methods are at 2x to 4x bitrates of ours.

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JBHI Journal 2023 Journal Article

Real-Time Ground Reaction Force and Knee Extension Moment Estimation During Drop Landings Via Modular LSTM Modeling and Wearable IMUs

Tao Sun
Dongxuan Li
Bingfei Fan
Tian Tan
Peter B. Shull

This work investigates real-time estimation of vertical ground reaction force (vGRF) and external knee extension moment (KEM) during single- and double-leg drop landings via wearable inertial measurement units (IMUs) and machine learning. A real-time, modular LSTM model with four sub-deep neural networks was developed to estimate vGRF and KEM. Sixteen subjects wore eight IMUs on the chest, waist, right and left thighs, shanks, and feet and performed drop landing trials. Ground embedded force plates and an optical motion capture system were used for model training and evaluation. During single-leg drop landings, accuracy for the vGRF and KEM estimation was $R^{2}$ = 0. 88 $\pm$ 0. 12 and $R^{2}$ = 0. 84 $\pm$ 0. 14, respectively, and during double-leg drop landings, accuracy for the vGRF and KEM estimation was $R^{2}$ = 0. 85 $\pm$ 0. 11 and $R^{2}$ = 0. 84 $\pm$ 0. 12, respectively. The best vGRF and KEM estimations of the model with the optimal LSTM unit number (130) require eight IMUs placed on the eight selected locations during single-leg drop landings. During double-leg drop landings, the best estimation on a leg only needs five IMUs placed on the chest, waist, and the leg's shank, thigh, and foot. The proposed modular LSTM-based model with optimally-configurable wearable IMUs can accurately estimate vGRF and KEM in real-time with relatively low computational cost during single- and double-leg drop landing tasks. This investigation could potentially enable in-field, non-contact anterior cruciate ligament injury risk screening and intervention training programs.

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JBHI Journal 2022 Journal Article

Transfer Learning Improves Accelerometer-Based Child Activity Recognition via Subject-Independent Adult-Domain Adaption

Jinxuan Li
Peiqi Kang
Tian Tan
Peter B. Shull

Wearable activity recognition can collate the type, intensity, and duration of each child’s physical activity profile, which is important for exploring underlying adolescent health mechanisms. Traditional machine-learning-based approaches require large labeled data sets; however, child activity data sets are typically small and insufficient. Thus, we proposed a transfer learning approach that adapts adult-domain data to train a high-fidelity, subject-independent model for child activity recognition. Twenty children and twenty adults wore an accelerometer wristband while performing walking, running, sitting, and rope skipping activities. Activity classification accuracy was determined via the traditional machine learning approach without transfer learning and with the proposed subject-independent transfer learning approach. Results showed that transfer learning increased classification accuracy to 91. 4% as compared to 80. 6% without transfer learning. These results suggest that subject-independent transfer learning can improve accuracy and potentially reduce the size of the required child data sets to enable physical activity monitoring systems to be adopted more widely, quickly, and economically for children and provide deeper insights into injury prevention and health promotion strategies.

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JBHI Journal 2021 Journal Article

Accurate Impact Loading Rate Estimation During Running via a Subject-Independent Convolutional Neural Network Model and Optimal IMU Placement

Tian Tan
Zachary A. Strout
Peter B. Shull

Objective: Enable accurate estimation of vertical average loading rate (VALR) in runners with one or more wearable inertial measurement units (IMUs). Methods: A subject-independent convolutional neural network (CNN) model was developed to estimate VALR from wearable IMUs. Fifteen runners wore IMUs at the trunk, pelvis, thigh, shank, and foot and ran on an instrumented treadmill for combinations of the following conditions: foot-strike (forefoot, mid-foot, rear-foot), step rate (90% to 110% of baseline), running speed (2. 4 m/s and 2. 8 m/s) and footwear (standard and minimalist running shoes). Thirty-one IMU placement configurations with combinations of one to five IMUs were evaluated. VALR estimations from the wearable IMUs were compared with force-plate VALR measurements. Results: VALR estimations via the subject-independent CNN model with a single shank-worn IMU were highly correlated ( ρ = 0. 94) with force-plate VALR measurements and were substantially higher than previously reported peak tibial acceleration correlations with force-plate VALR measurements from shank-worn accelerometers ( ρ = 0. 44–0. 66). Correlation results from the CNN model for a single IMU placed at the foot, pelvis, trunk, and thigh were ρ = 0. 91, 0. 76, 0. 69, and 0. 65, respectively. There was no improvement in accuracy from the shank-worn IMU when adding 1–4 additional IMUs from the trunk, pelvis, thigh, or foot. Conclusion: The proposed subject-independent CNN model with a single shank-worn IMU provides more accurate estimation of VALR than previous wearable sensing approaches. Significance: This could enable runners to more accurately assess impact loading rates and potentially provide insights into running-related injury risk and prevention.

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NeurIPS Conference 2020 Conference Paper

Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning

Tianren Zhang
Shangqi Guo
Tian Tan
Xiaolin Hu
Feng Chen

Goal-conditioned hierarchical reinforcement learning (HRL) is a promising approach for scaling up reinforcement learning (RL) techniques. However, it often suffers from training inefficiency as the action space of the high-level, i. e. , the goal space, is often large. Searching in a large goal space poses difficulties for both high-level subgoal generation and low-level policy learning. In this paper, we show that this problem can be effectively alleviated by restricting the high-level action space from the whole goal space to a k-step adjacent region of the current state using an adjacency constraint. We theoretically prove that the proposed adjacency constraint preserves the optimal hierarchical policy in deterministic MDPs, and show that this constraint can be practically implemented by training an adjacency network that can discriminate between adjacent and non-adjacent subgoals. Experimental results on discrete and continuous control tasks show that incorporating the adjacency constraint improves the performance of state-of-the-art HRL approaches in both deterministic and stochastic environments.

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AAAI Conference 2020 Conference Paper

Parameterized Indexed Value Function for Efficient Exploration in Reinforcement Learning

Tian Tan
Zhihan Xiong
Vikranth R. Dwaracherla

It is well known that quantifying uncertainty in the actionvalue estimates is crucial for efﬁcient exploration in reinforcement learning. Ensemble sampling offers a relatively computationally tractable way of doing this using randomized value functions. However, it still requires a huge amount of computational resources for complex problems. In this paper, we present an alternative, computationally efﬁcient way to induce exploration using index sampling. We use an indexed value function to represent uncertainty in our actionvalue estimates. We ﬁrst present an algorithm to learn parameterized indexed value function through a distributional version of temporal difference in a tabular setting and prove its regret bound. Then, in a computational point of view, we propose a dual-network architecture, Parameterized Indexed Networks (PINs), comprising one mean network and one uncertainty network to learn the indexed value function. Finally, we show the efﬁcacy of PINs through computational experiments.

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