@inproceedings{lokare_samadi_zhong_gonzalez_mohammadzadeh_lobaton_2017, title={Energy-efficient activity recognition via multiple time-scale analysis}, url={http://dx.doi.org/10.1109/ssci.2017.8285176}, DOI={10.1109/ssci.2017.8285176}, abstractNote={In this work, we propose a novel power-efficient strategy for supervised human activity recognition using a multiple time-scale approach, which takes into account various window sizes. We assess the proposed methodology on our new multimodal dataset for activities of daily life (ADL), which combines the use of physiological and inertial sensors from multiple wearable devices. We aim to develop techniques that can run efficiently in wearable devices for real-time activity recognition. Our analysis shows that the proposed approach Sequential Maximum-Likelihood (SML) achieves high F1 score across all activities while providing lower power consumption than the standard Maximum-Likelihood (ML) approach.}, booktitle={2017 IEEE Symposium Series on Computational Intelligence (SSCI)}, publisher={IEEE}, author={Lokare, N. and Samadi, S. and Zhong, Boxuan and Gonzalez, L. and Mohammadzadeh, F. and Lobaton, E.}, year={2017}, pages={1466–1472} }
 @inproceedings{richmond_lokare_lobaton_2017, title={Robust trajectory-based density estimation for geometric structure recovery}, url={http://dx.doi.org/10.23919/eusipco.2017.8081400}, DOI={10.23919/eusipco.2017.8081400}, abstractNote={We propose a method to both quickly and robustly extract geometric information from trajectory data. While point density may be of interest in some applications, trajectories provide different guarantees about our data such as path densities as opposed to location densities provided by points. We aim to utilize the concise nature of quadtrees in two dimensions to reduce run time complexity of counting trajectories in a neighborhood. We compare the accuracy of our methodology to a common current practice for subsampling a structure. Our results show that the proposed method is able to capture the geometric structure. We find an improvement in performance over the current practice in that our method is able to extract only the salient data and ignore trajectory outliers.}, booktitle={2017 25th European Signal Processing Conference (EUSIPCO)}, publisher={IEEE}, author={Richmond, Turner and Lokare, Namita and Lobaton, Edgar}, year={2017}, month={Aug}, pages={1210–1214} }
 @inproceedings{lokare_gonzalez_lobaton_2016, title={Comparing wearable devices with wet and textile electrodes for activity recognition}, url={http://dx.doi.org/10.1109/embc.2016.7591492}, DOI={10.1109/embc.2016.7591492}, abstractNote={This paper explores the idea of identifying activities from muscle activation which is captured by wearable ECG recording devices that use wet and textile electrodes. Most of the devices available today filter out the high frequency components to retain only the signal related to an ECG. We explain how the high frequency components that correspond to muscle activation can be extracted from the recorded signal and can be used to identify activities. We notice that is possible to obtain good performance for both the wet and dry electrodes. However, we observed that signals from the dry textile electrodes introduce less artifacts associated with muscle activation.}, booktitle={2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, publisher={IEEE}, author={Lokare, Namita and Gonzalez, Laura and Lobaton, Edgar}, year={2016}, month={Aug}, pages={3539–3542} }
 @article{ge_lokare_lobaton_2015, title={Non-Rigid Image Registration under Non-Deterministic Deformation Bounds}, volume={9287}, ISSN={["1996-756X"]}, url={http://dx.doi.org/10.1117/12.2072530}, DOI={10.1117/12.2072530}, abstractNote={Image registration aims to identify the mapping between corresponding locations in an anatomic structure. Most traditional approaches solve this problem by minimizing some error metric. However, they do not quantify the uncertainty behind their estimates and the feasibility of other solutions. In this work, it is assumed that two images of the same anatomic structure are related via a Lipschitz non-rigid deformation (the registration map). An approach for identifying point correspondences with zero false-negative rate and high precision is introduced under this assumption. This methodology is then extended to registration of regions in an image which is posed as a graph matching problem with geometric constraints. The outcome of this approach is a homeomorphism with uncertainty bounds characterizing its accuracy over the entire image domain. The method is tested by applying deformation maps to the LPBA40 dataset.}, journal={10TH INTERNATIONAL SYMPOSIUM ON MEDICAL INFORMATION PROCESSING AND ANALYSIS}, publisher={SPIE}, author={Ge, Qian and Lokare, Namita and Lobaton, Edgar}, editor={Romero, Eduardo and Lepore, NatashaEditors}, year={2015} }