With the advancements in methods for capturing 3D object motion, motion capture (MoCap) data are starting to be used beyond their traditional realm of animation and gaming in areas such as the arts, rehabilitation, automotive industry, remote interactions, and so on. As the amount of MoCap data increases, compression becomes crucial for further expansion and adoption of these technologies. In this paper, we extend our previous work on low-delay MoCap data compression by introducing two improvements. The first improvement is the bit allocation to long-term and short-term reference MoCap frames, which provides a 10-15% reduction in coded bitrate at the same quality. The second improvement is the post-processing in the form of motion-adaptive temporal low-pass filtering, which is able to provide another 9-13%savings in the bitrate. The experimental results also indicate that the proposed online MoCap codec is competitive with several state-of-the-art offline codecs. Overall, the proposed techniques integrate into a highly effective online MoCap codec that is suitable for low-delay applications, whose implementation is provided alongside this paper to aid further research in the field.
Radial peripapillary capillaries (RPCs) comprise a unique network of capillary beds within the retinal nerve fibre layer (RNFL) and play a critical role in satisfying the nutritional requirements of retinal ganglion cell (RGC) axons. Understanding the topographical and morphological characteristics of these networks through in vivo techniques may improve our understanding about the role of RPCs in RGC axonal health and disease. This study utilizes a novel, non-invasive and label-free optical imaging technique, speckle variance optical coherence tomography (svOCT), for quantitatively studying RPC networks in the human retina. Six different retinal eccentricities from 16 healthy eyes were imaged using svOCT. The same eccentricities were histologically imaged in 9 healthy donor eyes with a confocal scanning laser microscope. Donor eyes were subject to perfusion-based labeling techniques prior to retinal dissection, flat mounting and visualization with the microscope. Capillary density and diameter measurements from each eccentricity in svOCT and histological images were compared. Data from svOCT images were also analysed to determine if there was a correlation between RNFL thickness and RPC density. The results are as follows: (1) The morphological characteristics of RPC networks on svOCT images are comparable to histological images; (2) With the exception of the nasal peripapillary region, there were no significant differences in RPC density measurements between svOCT and histological images; (3) Capillary diameter measurements were significantly greater in svOCT images compared to histology; (4) There is a positive correlation between RPC density and RNFL thickness. The findings in this study suggest that svOCT is a reliable modality for analyzing RPC networks in the human retina. It may therefore be a valuable tool for aiding our understanding about vasculogenic mechanisms that are involved in RGC axonopathies. Further work is required to explore the reason for some of the quantitative differences between svOCT and histology.
The task of discriminating the motor imagery of different movements within the same limb using electroencephalography (EEG) signals is challenging because these imaginary movements have close spatial representations on the motor cortex area. There is, however, a pressing need to succeed in this task. The reason is that the ability to classify different same-limb imaginary movements could increase the number of control dimensions of a brain-computer interface (BCI). In this paper, we propose a 3-class BCI system that discriminates EEG signals corresponding to rest, imaginary grasp movements, and imaginary elbow movements. Besides, the differences between simple motor imagery and goal-oriented motor imagery in terms of their topographical distributions and classification accuracies are also being investigated. To the best of our knowledge, both problems have not been explored in the literature. Based on the EEG data recorded from 12 able-bodied individuals, we have demonstrated that same-limb motor imagery classification is possible. For the binary classification of imaginary grasp and elbow (goal-oriented) movements, the average accuracy achieved is 66.9%. For the 3-class problem of discriminating rest against imaginary grasp and elbow movements, the average classification accuracy achieved is 60.7%, which is greater than the random classification accuracy of 33.3%. Our results also show that goal-oriented imaginary elbow movements lead to a better classification performance compared to simple imaginary elbow movements. This proposed BCI system could potentially be used in controlling a robotic rehabilitation system, which can assist stroke patients in performing task-specific exercises.
In this paper, we study the compressed sensing reconstruction problem with generalized elastic net prior (GENP), where a sparse signal is sampled via a noisy underdetermined linear observation system, and an additional initial estimation of the signal (the GENP) is available during the reconstruction. We first incorporate the GENP into the LASSO and the approximate message passing (AMP) frameworks, denoted by GENP-LASSO and GENP-AMP respectively. We then focus on GENP-AMP and investigate its parameter selection, state evolution, and noise-sensitivity analysis. A practical parameterless version of the GENP-AMP is also developed, which does not need to know the sparsity of the unknown signal and the variance of the GENP. Simulation results with 1-D data and two different imaging applications are presented to demonstrate the efficiency of the proposed schemes.
Body motion data registered by wearable sensors can provide objective feedback to patients on the effectiveness of the rehabilitation interventions they undergo. Such a feedback may motivate patients to keep increasing the amount of exercise they perform, thus facilitating their recovery during physical rehabilitation therapy. In this work, we propose a novel wearable and affordable system which can predict different postures of the upper-extremities by classifying force myographic (FMG) signals of the forearm in real-time.
An easy to use force sensor resistor (FSR) strap to extract the upper-extremities FMG signals was prototyped. The FSR strap was designed to be placed on the proximal portion of the forearm and capture the activities of the main muscle groups with eight force input channels. The non-kernel based extreme learning machine (ELM) classifier with sigmoid based function was implemented for real-time classification due to its fast learning characteristics. A test protocol was designed to classify in real-time six upper-extremities postures that are needed to successfully complete a drinking task, which is a functional exercise often used in constraint-induced movement therapy. Six healthy volunteers participated in the test. Each participant repeated the drinking task three times. FMG data and classification results were recorded for analysis.
The obtained results confirmed that the FMG data captured from the FSR strap produced distinct patterns for the selected upper-extremities postures of the drinking task. With the use of the non-kernel based ELM, the postures associated to the drinking task were predicted in real-time with an average overall accuracy of 92.33% and standard deviation of 3.19%.
This study showed that the proposed wearable FSR strap was able to detect eight FMG signals from the forearm. In addition, the implemented ELM algorithm was able to correctly classify in real-time six postures associated to the drinking task. The obtained results therefore point out that the proposed system has potential for providing instant feedback during functional rehabilitation exercises.
The aim of the present study is to demonstrate, through tests with healthy volunteers, the feasibility of potentially assisting individuals with neurological disorders via a portable assistive technology for the upper extremities (UE). For this purpose the task of independently drinking a glass of water was selected, as it is one of the most basic and vital activities of the daily living that is unfortunately not achievable by individuals severely affected by stroke.
To accomplish the aim of this study we introduce a wearable and portable system consisting of a novel lightweight Robotic Arm Orthosis (RAO), a Functional Electrical Stimulation (FES) system, and a simple wireless Brain-Computer Interface (BCI). This system is able to process electroencephalographic (EEG) signals and translate them into motions of the impaired arm. Five healthy volunteers participated in this study and were asked to simulate stroke patient symptoms with no voluntary control of their hand and arm. The setup was designed such as the volitional movements of the healthy volunteers’ UE did not interfere with the evaluation of the proposed assistive system. The drinking task was split into eleven phases of which seven were executed by detecting EEG-based signals through the BCI. The user was asked to imagine UE motion related to the specific phase of the task to be assisted. Once detected by the BCI the phase was initiated. Each phase was then terminated when the BCI detected the volunteers clenching their teeth.
The drinking task was completed by all five participants with an average time of 127 seconds with a standard deviation of 23 seconds. The incremental motions of elbow extension and elbow flexion were the primary limiting factors for completing this task faster. The BCI control along with the volitional motions also depended upon the users pace, hence the noticeable deviation from the average time.
Through tests conducted with healthy volunteers, this study showed that our proposed system has the potential for successfully assisting individuals with neurological disorders and hemiparetic stroke to independently drink from a glass.
In radiotherapy, temporary translocations of the internal organs and tumor induced by respiratory and cardiac activities can undesirably lead to significantly lower radiation dose on the targeted tumor but more harmful radiation on surrounding healthy tissues. Respiratory and cardiac gated radiotherapy offers a potential solution for the treatment of tumors located in the upper thorax. The present study focuses on the design and development of simultaneous acquisition of respiratory and cardiac signal using electrical impedance technology for use in dual gated radiotherapy.
An electronic circuitry was developed for monitoring the bio-impedance change due to respiratory and cardiac motions and extracting the cardiogenic ECG signal. The system was analyzed in terms of reliability of signal acquisition, time delay, and functionality in a high energy radiation environment. The resulting signal of the system developed was also compared with the output of the commercially available Real-time Position Management™ (RPM) system in both time and frequency domains.
The results demonstrate that the bioimpedance-based method can potentially provide reliable tracking of respiratory and cardiac motion in humans, alternative to currently available methods. When compared with the RPM system, the impedance-based system developed in the present study shows similar output pattern but different sensitivities in monitoring different respiratory rates. The tracking of cardiac motion was more susceptible to interference from other sources than respiratory motion but also provided synchronous output compared with the ECG signal extracted. The proposed hardware-based implementation was observed to have a worst-case time delay of approximately 33 ms for respiratory monitoring and 45 ms for cardiac monitoring. No significant effect on the functionality of the system was observed when it was tested in a radiation environment with the electrode lead wires directly exposed to high-energy X-Rays.
The developed system capable of rendering quality signals for tracking both respiratory and cardiac motions can potentially provide a solution for simultaneous dual-gated radiotherapy.
Disorders associated with excessive swelling of the lower extremities are common. They can be associated with pain, varicose veins, reduced blood pressure when standing and may cause syncope or fainting. The common physical remedy to these disorders is the use of compression stockings and pneumatic compression leg massagers, which both attempt to limit blood pooling and capillary filtration in the lower limbs. However, compression stockings provide a constant pressure, and their efficiency has been challenged according to some recent studies. Air compression leg massagers on the other hand, restricts patient mobility. In this work we therefore present an innovative active compression bandage based on the use of a smart materials technology that could produce intermittent active pressure to mitigate the symptoms of lower extremity disorders.
An active compression bandage (ACB), actuated by shape memory alloy (SMA) wires, was designed and prototyped. The ACB was wrapped around a calf model to apply an initial pressure comparable to the one exerted by commercial compression stockings. The ACB was controlled to apply different values of compression. A data acquisition board and a LabVIEW program were used to acquire both the pressure data exerted by the ACB and the electrical current required to actuate the SMA wires. An analytical model of the ACB based on a SMA constitutive model was developed. An optimizer was implemented to identify optimal parameters of the model to best estimate the performance of the ACB.
The maximum increase in pressure due to the SMA wires activation was 40.8% higher than the initially applied pressure to the calf model. The analytical model of the ACB estimated the behaviour of the ACB with less than 0.32 mmHg difference with the experimental results.
The prototyped ACB was able to apply an initial compression comparable to the one applied by commercial compression stockings. Activation of the ACB resulted in an increase of compression up to 9.06 mmHg. Comparison between analytical and experimental results showed the analytical model was suitable to predict the behaviour of the ACB.
Older adults living in long term care (LTC) settings are vulnerable to fall-related injuries. There is a need to develop and implement evidence-based approaches to address fall injury prevention in LTC. Knowledge translation (KT) interventions to support the uptake of evidence-based approaches to fall injury prevention in LTC need to be responsive to the learning needs of LTC staff and use mediums, such as videos, that are accessible and easy-to-use. This article describes the development of two unique educational videos to promote fall injury prevention in long-term care (LTC) settings. These videos are unique from other fall prevention videos in that they include video footage of real life falls captured in the LTC setting.
Two educational videos were developed (2012–2013) to support the uptake of findings from a study exploring the causes of falls based on video footage captured in LTC facilities. The videos were developed by: (1) conducting learning needs assessment in LTC settings via six focus groups (2) liaising with LTC settings to identify learning priorities through unstructured conversations; and (3) aligning the content with principles of adult learning theory.
The videos included footage of falls, interviews with older adults and fall injury prevention experts. The videos present evidence-based fall injury prevention recommendations aligned to the needs of LTC staff and: (1) highlight recommendations deemed by LTC staff as most urgent (learner-centered learning); (2) highlight negative impacts of falls on older adults (encourage meaning-making); and, (3) prompt LTC staff to reflect on fall injury prevention practices (encourage critical reflection).
Educational videos are an important tool available to researchers seeking to translate evidence-based recommendations into LTC settings. Additional research is needed to determine their impact on practice.
Altered brain development is evident in children born very preterm (24–32 weeks gestational age), including reduction in gray and white matter volumes, and thinner cortex, from infancy to adolescence compared to term-born peers. However, many questions remain regarding the etiology. Infants born very preterm are exposed to repeated procedural pain-related stress during a period of very rapid brain development. In this vulnerable population, we have previously found that neonatal pain-related stress is associated with atypical brain development from birth to term-equivalent age. Our present aim was to evaluate whether neonatal pain-related stress (adjusted for clinical confounders of prematurity) is associated with altered cortical thickness in very preterm children at school age.
42 right-handed children born very preterm (24–32 weeks gestational age) followed longitudinally from birth underwent 3-D T1 MRI neuroimaging at mean age 7.9 yrs. Children with severe brain injury and major motor/sensory/cognitive impairment were excluded. Regional cortical thickness was calculated using custom developed software utilizing FreeSurfer segmentation data. The association between neonatal pain-related stress (defined as the number of skin-breaking procedures) accounting for clinical confounders (gestational age, illness severity, infection, mechanical ventilation, surgeries, and morphine exposure), was examined in relation to cortical thickness using constrained principal component analysis followed by generalized linear modeling.
After correcting for multiple comparisons and adjusting for neonatal clinical factors, greater neonatal pain-related stress was associated with significantly thinner cortex in 21/66 cerebral regions (p-values ranged from 0.00001 to 0.014), predominately in the frontal and parietal lobes.
In very preterm children without major sensory, motor or cognitive impairments, neonatal pain-related stress appears to be associated with thinner cortex in multiple regions at school age, independent of other neonatal risk factors.