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Monitoring in-bed pose estimation based on the Internet of Medical Things (IoMT) and ambient technology has a significant impact on many applications such as sleep-related disorders including obstructive sleep apnea syndrome, assessment of sleep quality, and health risk of pressure ulcers. In this research, a new multimodal in-bed pose estimation has been proposed using a deep learning framework. The Simultaneously-collected multimodal Lying Pose (SLP) dataset has been used for performance evaluation of the proposed framework where two modalities including long wave infrared (LWIR) and depth images are used to train the proposed model. The main contribution of this research is the feature fusion network and the use of a generative model to generate RGB images having similar poses to other modalities (LWIR/depth). The inclusion of a generative model helps to improve the overall accuracy of the pose estimation algorithm. Moreover, the method can be generalized for situations to recover human pose both in home and hospital settings under various cover thickness levels. The proposed model is compared with other fusion-based models and shows an improved performance of 97.8% at PCKh$@$0.5. In addition, performance has been evaluated for different cover conditions, and under home and hospital environments which present improvements using our proposed model.

Gait analysis continues to be an important technique for many clinical applications to diagnose and monitor certain diseases. Many mental and physical abnormalities cause measurable differences in a person's gait. Gait analysis has applications in sport, computer games, physical rehabilitation, clinical assessment, surveillance, human recognition, modeling, and many other fields. There are established methods using various sensors for gait analysis, of which accelerometers are one of the most often employed. Accelerometer sensors are generally more user friendly and less invasive. In this paper, we review research regarding accelerometer sensors used for gait analysis with particular focus on clinical applications. We provide a brief introduction to accelerometer theory followed by other popular sensing technologies. Commonly used gait phases and parameters are enumerated. The details of selecting the papers for review are provided. We also review several gait analysis software. Then we provide an extensive report of accelerometry-based gait analysis systems and applications, with additional emphasis on trunk accelerometry. We conclude this review with future research directions.

Many wearable devices include PPG (photoplethysmography) sensors for non-invasive heart rate monitoring. However, PPG signals are heavily corrupted by motion interference, and rely on simultaneous motion measurements to remove the interference. Accelerometers are used commonly, but cannot differentiate between acceleration due to movement and acceleration due to gravity. This paper compares measurements of motion using accelerometers and gyroscopes to give a more complete estimate of wrist motion. Results show the two sensor signals are very different, with low correlations present. When used in a wrist PPG heart rate algorithm gyroscope motion estimates obtain better performance in half of the cases.

Surgical training and practice is stressful, but adaptive changes in the stress circuitry (e.g. perceptual, physiological, hormonal, neural) could support skill development. This work examined skill acquisition and stress adaptations in novice surgeons during laparoscopic surgery (LS) training and detraining. Twelve medical students were assessed for skill performance after 2 h (BASE), 5 h (MID) and 8 h (POST) of LS training in weeks 1–3, and then after 4 weeks of no training (RETEST). The stress outcomes included state anxiety, perceived stress and workload, heart rate (HR), heart rate variability (HRV), and salivary testosterone and cortisol concentrations. Functional near-infrared spectroscopy was used to assess cortical oxygenation change, as a marker of prefrontal cortex (PFC) activity. Skill performance improved in every session from BASE (p< 0.01), with corresponding decreases in state anxiety, stress, workload, low- and high-frequency HRV in the MID, POST and/or RETEST sessions (p< 0.05). Left and right PFC were symmetrically activated within each testing session (p< 0.01). The stress and workload measures predicted skill performance and changes over time (p< 0.05), with state anxiety, mean HR and the HRV measures also showing some predictive potential (p< 0.10). A 3-week LS training programme promoted stress-related adaptations likely to directly, or indirectly, support the acquisition of new surgical skills, and many outcomes were retained after a 4-week period without further LS training. These results have implications for medical training and education (e.g. distributed training for skill development and maintenance, stress resource and management training) and highlighted possible areas for new research (e.g. longitudinal stress and skill profiling).

To develop and validate a robust, objective mobility assessment tool, Hamlyn Mobility Score (HMS), using a wearable motion sensor.