multimodal sensor fusion
Google Scholar. Data 9, 474. https://doi.org/10.1038/s41597-022-01573-2 (2022). Bao, H. et al. (8) has converged during the optimization process, the samples are decoded/recovered for modality 1 and modality 2 using their respective decoders \(\hat{x}_{m} = \psi _{m}(\hat{z}_{MAP})\). Front. van Smeden, M., Penning de Vries, B. Med. Intell. Nat. This work proposes a high-performing and transferable occupancy detection framework that combines sensor data from different data modalities, including time series environmental data (temperature, humidity, and illuminance), image data, and acoustic energy data using ensemble method. Compared to the aforementioned works12,13,14,15,16 which consider supervised models with feature-level fusion or decision-level fusion, our technique, in contrast, performs multimodal sensor fusion in the latent representation space leveraging a self-supervised generative model. Pattern Anal. The All of Us Research Program. On CSI and passive Wi-Fi radar for opportunistic physical activity recognition. 1, alongside two main groups of approaches to sensor fusion. Lancet 393, 1297 (2019). Dosovitskiy, A. et al. The availability of multimodal data in these datasets may help achieve better diagnostic performance across a range of different tasks. NPJ Flex. Damask, A. et al. Memmesheimer, R., Theisen, N. & Paulus, D. Gimme signals: Discriminative signal encoding for multimodal activity recognition. Accuracy of identifying incident stroke cases from linked health care data in UK Biobank. 12, 690049 (2021). S6 in the Supplementary Information document, with further experimental results in Sects. However, some types of health datasuch as user-generated and de-identified health dataare not covered by this regulation, which poses a risk of reidentification by combining information from multiple sources. Deep learning has shown great potential in smart agriculture, especially in the field of pest recognition. For example, although large sample sizes (in the range of hundreds of thousands to millions) are desirable in most cases for the training of AI models (especially multimodal AI models), the costs of achieving deep phenotyping and good longitudinal follow-up scales rapidly with larger numbers of participants, becoming financially unsustainable unless automated methods of data collection are put in place. Combining these data with those derived from EHRsusing standards such as the Fast Healthcare Interoperability Resources, a global industry standard for exchanging healthcare data51to query relevant information about a patients underlying disease risk could create a more personalized remote monitoring experience for patients and caregivers. Mol. FDA approves first treatment for a form of Batten disease https://www.fda.gov/news-events/press-announcements/fda-approves-first-treatment-form-batten-disease#:~:text=The%20U.S.%20Food%20and%20Drug,specific%20form%20of%20Batten%20disease (2017). Genet. For each modality, the data generative model \(p(x_m|z), x_m \in \mathbb {R}^N\) is a one-layer multilayer perceptron (MLP) with random weights. vol. Recipes for building an open-domain Chatbot. Pandit, J. [35] proposed an adaptive multimodal fusion architecture for HAR using videos and inertial data. Nagrani, A. et al. JAMA Oncol. J. Med. Int. Importantly, the input to the Perceiver architectures are modality-agnostic byte arrays, which are condensed through an attention bottleneck (that is, an architecture feature that restricts the flow of information, forcing models to condense the most relevant) to avoid size-dependent large memory costs (Fig. 3, 119 (2020). Zhang, Y., Jiang, H., Miura, Y., Manning, C. D. & Langlotz, C. P. Contrastive learning of medical visual representations from paired images and text. & Lempitsky, V. Deep image prior. Med. Assume the data generative process so that latent and common cause Z gives rise to \(X_m\), which in turn produces observed \(Y_m\), i.e. Google Scholar. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Many other important challenges relating to multimodal model architectures remain. Integrating data from multiple sources to develop digital twin models using AI tools has already been proposed in precision oncology and cardiovascular health67,68. Paul Pu Liang, Yun Cheng, Ruslan Salakhutdinov, Louis-Philippe Morency. b, Simplified illustration of the conceptual framework behind the multimodal multitask architectures (for example, Gato), within a hypothetical medical example: distinct input modalities ranging from images, text and actions are tokenized and fed to the network as input sequences, with masked shifted versions of these sequences fed as targets (that is, the network only sees information from previous time points to predict future actions, only previous words to predict the next or only the image to predict text); the network then learns to handle multiple modalities and tasks. Iii, H. D. & Singh, A.) 68, 5974 (PMLR, 1819 August 2017). Potter, D. et al. & Agarwal, S. Health-focused conversational agents in person-centered care: a review of apps. Evaluation and comparison of multi-omics data integration methods for cancer subtyping. & Vogt, J.E. Generalized multimodal ELBO. 117, 489501 (2014). Scheibner, J. et al. Jaegle, A. et al. B. L., Nab, L. & Groenwold, R. H. H. Approaches to addressing missing values, measurement error, and confounding in epidemiologic studies. 26, 13851391 (2020). This article presents a multi-modal sensor fusion scheme that, based on standard production car sensors and an inertial measurement unit, estimates the three-dimensional vehicle velocity and attitude angles (pitch and roll). Multi-modal Sensor Fusion-Based Deep Neural Network for End-to-end A study from DeepMind, in fact, suggested that curating higher-quality imagetext datasets may be more important than generating large single-modality datasets, and other aspects of algorithm development and training119. 216, 574578 (2015). Mach. And crucially our technique attempts to directly compute the MAP (maximum a posteriori) estimate. 18, 463477 (2019). Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases. Data fusion uses the data and results from multisensor detection to form more accurate and credible conclusions and quality that cannot be obtained from a single sensor. A subset of individuals also underwent brain magnetic resonance imaging (MRI), cardiac MRI, abdominal MRI, carotid ultrasound and dual-energy X-ray absorptiometry, including repeat imaging across at least two time points85. In Precup, D. & Teh, Y.W. Mach. Since completing this Review, J.N.A. IEEE Access 8, 28472868. 48, 12791283 (2016). In this section, we review the state-of-the-art in three areas directly relevant to our contributions: multimodal generative modeling, sensor fusion, and compressed sensing. Masison, J. et al. Diversity and inclusion for the All of Us research program: a scoping review. Pallmann, P. et al. Article 142, 7782 (2012). For example, previous studies have demonstrated that by utilizing only a little background information about participants, an adversary could re-identify those in large datasets (for example, the Netflix prize dataset), uncovering sensitive information about the individuals163. In International Conference on Learning Representations (ICLR, 2022). Karczewski, K. J. This is because the modalities may vastly differ in characteristics, including dimensionality, data distribution, and sparsity6. 1a). This multimodal sensor can permit the real-time acquisition of the . Biotechnol. EBioMedicine 73, 103613 (2021). NPJ Digit. CAS Nat. In International Conference on Learning Representations (ICLR, 2021). Fisher, C. K., Smith, A. M. & Walsh, J. R. Machine learning for comprehensive forecasting of Alzheimers disease progression. This deep learning model aims to address two data-fusion problems: cross-modality and shared-modality representational learning. Epidemiol. Rep. 9, 16884 (2019). 30 (Curran Associates, Inc., 2017). All are trained in a conventional supervised fashion from scratch using the ResNet-18 backbone and a linear classification head is appended on top of it consisting of a hidden linear layer of 128 units and a linear output layer of 6 nodes (for classifying 6 human activities). Graph neural networks have been recently proposed to overcome the problem of missing or irregularly sampled data from multiple health sensors, by leveraging information from the interconnection between these42. How edge computing is driving advancements in healthcare analytics; https://www.intel.com/content/www/us/en/healthcare-it/edge-analytics.html (11 March 2022.). This is a common issue whenusing gradient descentin optimization problems, with existing solutions to mitigate it. Multimodal biomedical AI. Med. The simplest approach involves concatenating input modalities or features before any processing (early fusion). Multimodal sensor fusion in the latent representation space, $$\begin{aligned} p\left( z,x_{1:M},y_{1:M}\right) = p\left( z\right) \prod _{m=1}^M{ p(y_{m}|x_{m}) p(x_{m}|z) }. & Steinhubl, S. R. Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study. compressed sensing. The technique relies on a two-stage process. It can be observed that the samples can be recovered with very low reconstruction error when the number of measurements is as low as 196 (0.39%). Health Technol. Published in: IEEE Sensors Journal ( Volume: 21 , Issue: 10 , 15 May 2021 ) Article #: 1(a). This granularity of data is particularly useful for the data science and machine learning community, and MIMIC has become one of the benchmark datasets for AI models aiming to predict the development of clinical events such as kidney failure, or outcomes such as survival or readmissions97,98. In medicine, a model architecture called CycleGANs, trained on unpaired contrast and non-contrast CT scans, has been used to generate synthetic non-contrast or contrast CT scans121, with this approach showing improvements, for instance, in COVID-19 diagnosis122. Gichoya, J. W. et al. The integration of these very distinct types of data remains challenging. In the USA, the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule is the fundamental legislation to protect privacy of health data. As a result, imputation is a pervasive preprocessing step in many biomedical scientific fields, ranging from genomics to clinical data. Sci. Currently in its fourth version, MIMIC is an open-source database that contains de-identified data from thousands of patients who were admitted to the critical care units of the Beth Israel Deaconess Medical Center, including demographic information, EHR data (for example, diagnosis codes, medications ordered and administered, laboratory data and physiological data such as blood pressure or intracranial pressure values), imaging data (for example, chest radiographs)96 and, in some versions, natural language text such as radiology reports and medical notes. In these works, a modality can refer to natural images, text, captions, labels or visual and non-visual attributes of a person. Specifically, our sensor fusion problem amounts to finding the maximum a posteriori (MAP) \(\hat{z}_{MAP}\) estimate of the latent cause for a given (\(i^{th}\)) data point \(Y_{1:M}=y^{(i)}_{1:M}\): The above MAP estimation problem is hard, and we will resort to approximations detailed in the sections below. Linguist. First we build a joint model which approximates Eq. A strong modality can be used to aid the recovery of another modality that is lossy or less informative (weak modality). J. Epidemiol. The journal is intended to present within a single forum all of the developments in the field of multi-sensor, multi-source, multi-process information fusion and thereby promote the synergism among the many disciplines that are contributing to its growth.The journal is the premier vehicle for disseminating information on all aspects of research and development in the field of information fusion. Both modalities are successfully recovered from the latent representation space, even though the initial guess \(z^0\) is far from the true modality. Jaegle, A. et al. Furthermore, we assume that \(p(x_{m}|z)= \delta (x_{m}-\psi _{m}(z))\). An integrated genomic analysis of human glioblastoma multiforme. There are large-scale efforts to provide meaningful harmonization to biomedical datasets, such as the Observational Medical Outcomes Partnership Common Data Model developed by the Observational Health Data Sciences and Informatics collaboration140. Feature fusion, as depicted in Fig. Observational Health Data Sciences and Informatics (OHDSI): opportunities for observational researchers. Kihara, Y. et al. Hum. Ophthalmology https://doi.org/10.1016/j.ophtha.2022.02.017 (2022). The authors of16 proposed a decision-level sensor fusion network for HAR using LiDAR and visual sensors. 168). A., Mallofr, A. C. & Ptzold, M. WiWeHAR: Multimodal human activity recognition using Wi-Fi and wearable sensing modalities. In particular, selection bias is a common type of bias in large biobank studies, and has been reported as a problem, for example, in the UK Biobank158. Theory 52, 12891306. 27, 20652066 (2021). Multi-modal sensor fusion for highly accurate vehicle motion state The multimodal data fusion algorithms that process multimodal signals from multimodal sensors to achieve object recognition and decision making are also presented. Derivation of Eq. In the last 20 years, many national and international studies have collected multimodal data with the ultimate goal of accelerating precision health (Table 1). Preprint at https://arxiv.org/abs/2107.07651 (2021). Attention Bottlenecks for Multimodal Fusion Arsha Nagrani, Shan Yang, Anurag Arnab, Aren Jansen, Cordelia Schmid, Chen Sun Humans perceive the world by concurrently processing and fusing high-dimensional inputs from multiple modalities such as vision and audio. We dedicate a section in the Supplementary Information document to the discussion on choices and implications for the approximation of variational posterior. & Fei-Fei, L. Illuminating the dark spaces of healthcare with ambient intelligence. In the second stage, we use the trained M-VAE and \(\chi _{1:M}\) to facilitate the fusion and reconstruction tasks. Therefore, the development of new methods to incentivize data sharing across sectors while preserving patient privacy is crucial. Patients with high genome-wide polygenic risk scores for coronary artery disease may receive greater clinical benefit from alirocumab treatment in the ODYSSEY OUTCOMES trial. Wang, Z., Wu, Y. San, O. Janssens, A. C. J. W. Validity of polygenic risk scores: are we measuring what we think we are? 2,906 spectrogram samples (each of 4s duration window) were generated for the 6 human activities and 80% of these were used as training data while the remaining 20% as testing data (random train-test split). Beyond genomics, imputation has also demonstrated utility for other types of medical data152. 41, 419428 (2015). DMVAE (Disentangled Multimodal VAE)10 uses a disentangled VAE approach to split up the private and shared (using PoE) latent spaces of multiple modalities, where the latent factor may be of both continuous and discrete nature. https://doi.org/10.1038/s41587-021-01075-3 (2021). Perceiver IO: a general architecture for structured inputs & outputs. Berisha, V. et al. Further, we survey the data, modeling and privacy challenges that must be overcome to realize the full potential of multimodal artificial intelligence in health. 11, 5749 (2020). Finally, swarm learning is a relatively novel approach that, similarly to federated learning, is also based on several individuals or organizations training a model on local data, but does not require a trusted central server because it replaces it with the use of blockchain smart contracts170. Fusion of different data modalities can be performed at different stages of the process. Reconstruction examples showing the benefit of multimodal system compared to a unimodal system. Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation. Some voices advocate for private patient ownership of the data, arguing that this approach would ensure the patients right to self-determination, support health data transactions and maximize patients benefit from data markets; while others suggest a non-property, regulatory model would better protect secure and transparent data use173,174. Whitelaw, S., Mamas, M. A., Topol, E. & Van Spall, H. G. C. Applications of digital technology in COVID-19 pandemic planning and response. Disparity of race reporting and representation in clinical trials leading to cancer drug approvals from 2008 to 2018. Sci. As expected, this approach constitutes a trade-off between the level of privacy obtained and the expected performance of the models. Schizophr. Nat. Chaudhuri, K. et al.) Genet. . supported this work. mSystems 3, e0003118 (2018). Choi, S. W., Mak, T. S. -H. & OReilly, P. F. Tutorial: a guide to performing polygenic risk score analyses. This offers the promise of marked reduction of cost, less requirement for healthcare workforce, avoidance of nosocomial infections and medical errors that occur in medical facilities, along with the comfort, convenience and emotional support of being with family members48. Preprint at https://doi.org/10.48550/arXiv.2004.06165 (2020). Assoc. This problem has also been pervasive in the scientific literature regarding COVID-19 (ref. Med. Ulyanov, D., Vedaldi, A. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. 34, 2420624221 (Curran Associates, Inc., 2021). A roadmap for multi-omics data integration using deep learning. This large biobank has collected multiple layers of data from participants, including sociodemographic and lifestyle information, physical measurements, biological samples, 12-lead electrocardiograms and EHR data82.
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