Sensor technology is becoming increasingly prevalent across a multitude of fields and industries. As a result, simultaneous recordings of multiple inter-correlated signals is becoming increasingly common. With this, more problems of a practical nature emerge due to sensor clock-drift, offsets, and other complications. Processing of multiple sensor data is often dependent on the data being properly alignedin the temporal dimension. The alignment process is a necessary step before the data can be evaluated properly but it is a time consuming process, often involving significant manual labor and expertise. Regularly used methods to align sensor signals have trouble addressing real-world issues such as morphological dissimilarities, excessive noise, or very long, raw sensor signals .  In this work, we present Deep Canonical Correlation Sensor Alignment (DCCA), a method that is specifically tailored to address these problems. It exploits common properties specific to misalignments produced by sensor circuitry, such as clock-drift and offsets . On a selection of artificial and real datasets  we demonstrate the performance  of DCCA under a variety of conditions  .
Sensor technologies are becoming increasingly prevalent in the biomedical field, with applications ranging from telemonitoring of people at risk, to using sensor derived information as objective endpoints in clinical trials. To fully utilize sensor information, signals from distinct sensors often have to be temporally aligned . However, due to imperfect oscillators and significant noise, commonly encountered with biomedical signals, temporal alignment of raw signals is an all but trivial problem, with, to-date, no generally applicable solution. In this work, we present Deep Canonical Correlation Alignment (DCCA), a novel, generally applicable solution for the temporal alignment of raw (biomedical) sensor signals. DCCA allows practitioners to directly align raw signals, from distinct sensors, without requiring deep domain knowledge . On a selection of artificial and real datasets , we demonstrate the performance and utility of DCCA under a variety of conditions . We compare the DCCA algorithm to other warping based methods, DCCA outperforms dynamic time warping and cross correlation based methods by an order of magnitude in terms of alignment error. DCCA performs especially well on almost periodic biomedical signals such as heart-beats and breathing patterns. In comparison to existing approaches, that are not tailored towards raw sensor data, DCCA is not only fast enough to work on signals with billions of data points but also provides automatic filtering and transformation functionalities, allowing it to deal with very noisy and even morphologically distinct signals .
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technologies are
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across a multitude of fields and industries. As a result, simultaneous recordings of multiple inter-correlated signals is becoming increasingly common. With this, more problems of a practical nature emerge due to sensor clock-drift, offsets, and other complications. Processing of multiple sensor data is often dependent on the data being properly alignedin the temporal dimension. The alignment process is a necessary step before the data can be evaluated properly but it is a time consuming process, often involving significant manual labor and expertise. Regularly used methods to align sensor signals have trouble addressing real-world issues such as morphological dissimilarities, excessive noise, or very long, raw sensor signals
in the biomedical field, with applications ranging from telemonitoring of people at risk, to using sensor derived information as objective endpoints in clinical trials. To fully utilize sensor information, signals from distinct sensors often have to be temporally aligned
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However, due to imperfect oscillators and significant noise, commonly encountered with biomedical signals, temporal alignment of raw signals is an all but trivial problem, with, to-date, no generally applicable solution.
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Sensor
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method that is specifically tailored to address these problems. It exploits common properties specific to misalignments produced by sensor circuitry, such as clock-drift and offsets
novel, generally applicable solution for the temporal alignment of raw (biomedical) sensor signals. DCCA allows practitioners to directly align raw signals, from distinct sensors, without requiring deep domain knowledge
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,
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and utility
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. We compare the DCCA algorithm to other warping based methods, DCCA outperforms dynamic time warping and cross correlation based methods by an order of magnitude in terms of alignment error. DCCA performs especially well on almost periodic biomedical signals such as heart-beats and breathing patterns. In comparison to existing approaches, that are not tailored towards raw sensor data, DCCA is not only fast enough to work on signals with billions of data points but also provides automatic filtering and transformation functionalities, allowing it to deal with very noisy and even morphologically distinct signals