Deep Learning for Health Informatics.

With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reshape the future of artificial intelligence. Rapid improvements in computational power, fast data storage, and parallelization have also contributed to the rapid uptake of the technology in addition to its predictive power and ability to generate automatically optimized high-level features and semantic interpretation from the input data. This article presents a comprehensive up-to-date review of research employing deep learning in health informatics, providing a critical analysis of the relative merit, and potential pitfalls of the technique as well as its future outlook. The paper mainly focuses on key applications of deep learning in the fields of translational bioinformatics, medical imaging, pervasive sensing, medical informatics, and public health.

A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices.

The increasing popularity of wearable devices in recent years means that a diverse range of physiological and functional data can now be captured continuously for applications in sports, wellbeing, and healthcare. This wealth of information requires efficient methods of classification and analysis where deep learning is a promising technique for large-scale data analytics. While deep learning has been successful in implementations that utilize high-performance computing platforms, its use on low-power wearable devices is limited by resource constraints. In this paper, we propose a deep learning methodology, which combines features learned from inertial sensor data together with complementary information from a set of shallow features to enable accurate and real-time activity classification. The design of this combined method aims to overcome some of the limitations present in a typical deep learning framework where on-node computation is required. To optimize the proposed method for real-time on-node computation, spectral domain preprocessing is used before the data are passed onto the deep learning framework. The classification accuracy of our proposed deep learning approach is evaluated against state-of-the-art methods using both laboratory and real world activity datasets. Our results show the validity of the approach on different human activity datasets, outperforming other methods, including the two methods used within our combined pipeline. We also demonstrate that the computation times for the proposed method are consistent with the constraints of real-time on-node processing on smartphones and a wearable sensor platform.

Gait Analysis From a Single Ear-Worn Sensor: Reliability and Clinical Evaluation for Orthopaedic Patients.

Objective assessment of detailed gait patterns after orthopaedic surgery is important for post-surgical follow-up and rehabilitation. The purpose of this paper is to assess the use of a single ear-worn sensor for clinical gait analysis. A reliability measure is devised for indicating the confidence level of the estimated gait events, allowing it to be used in free-walking environments and for facilitating clinical assessment of orthopaedic patients after surgery. Patient groups prior to or following anterior cruciate ligament (ACL) reconstruction and knee replacement were recruited to assess the proposed method. The ability of the sensor for detailed longitudinal analysis is demonstrated with a group of patients after lower limb reconstruction by considering parameters such as temporal and force-related gait asymmetry derived from gait events. The results suggest that the ear-worn sensor can be used for objective gait assessments of orthopaedic patients without the requirement and expense of an elaborate laboratory setup for gait analysis. It significantly simplifies the monitoring protocol and opens the possibilities for home-based remote patient assessment.

Gait parameter estimation from a miniaturized ear-worn sensor using singular spectrum analysis and longest common subsequence.

This paper presents a new approach to gait analysis and parameter estimation from a single miniaturized ear-worn sensor embedded with a triaxial accelerometer. Singular spectrum analysis combined with the longest common subsequence algorithm has been used as a basis for gait parameter estimation. It incorporates information from all axes of the accelerometer to estimate parameters including swing, stance, and stride times. Rather than only using local features of the raw signals, the periodicity of the signals is also taken into account. The hypotheses tested by this study include: 1) how accurate is the ear-worn sensor in terms of gait parameter extraction compared to the use of an instrumented treadmill; 2) does the ear-worn sensor provide a feasible option for assessment and quantification of gait pattern changes. Key gait events for normal subjects such as heel contact and toe off are validated with a high-speed camera, as well as a force-plate instrumented treadmill. Ten healthy adults walked for 20 min on a treadmill with an increasing incline of 2% every 2 min. The upper and lower limits of the absolute errors using 95% confidence intervals for swing, stance, and stride times were obtained as 35.5 ±3.99 ms, 36.9 ±3.84 ms, and 17.9 ±2.29 ms, respectively.

Markerless motion capture using appearance and inertial data.

Current monitoring techniques for biomechanical analysis typically capture a snapshot of the state of the subject due to challenges associated with long-term monitoring. Continuous long-term capture of biomechanics can be used to assess performance in the workplace and rehabilitation at home. Noninvasive motion capture using small low-power wearable sensors and camera systems have been explored, however, drift and occlusions have limited their ability to reliably capture motion over long durations. In this paper, we propose to combine 3D pose estimation from inertial motion capture with 2D pose estimation from vision to obtain more robust posture tracking. To handle the changing appearance of the human body due to pose variations and illumination changes, our implementation is based upon Least Soft-Threshold Squares Tracking. Constraints on the variation of the appearance model and estimated pose from an inertial motion capture system are used to correct 2D and 3D estimates simultaneously. We evaluate the performance of our method with three state-of-the-art trackers, Incremental Visual Tracking, Multiple Instance Learning, and Least Soft-Threshold Squares Tracking. In our experiments, we track the movement of the upper limbs. While the results indicate an improvement in tracking accuracy at some joint locations, they also show that the result can be further improved. Conclusions and further work required to improve our results are discussed.