A Novel Mobile Device-Based Approach to Quantitative Mobility Measurements for Power Wheelchair Users.

This study is motivated by the fact that there are currently no widely used applications available to quantitatively measure a power wheelchair user's mobility, which is an important indicator of quality of life. To address this issue, we propose an approach that allows power wheelchair users to use their own mobile devices, e.g., a smartphone or smartwatch, to non-intrusively collect mobility data in their daily life. However, the convenience of data collection brings substantial challenges in data analysis because the data patterns associated with wheelchair maneuvers are not as strong as other activities, e.g., walking, running, etc. In addition, the built-in sensors in different mobile devices create significant heterogeneity in terms of sensitivity, noise patterns, sampling settings, etc. To address the aforementioned challenges, we developed a novel approach composed of algorithms that work collaboratively to reduce noise, identify patterns intrinsic to wheelchair maneuvers, and finalize mobility analysis by removing spikes and dips caused by abrupt maneuver changes. We conducted a series of experiments to evaluate the proposed approach. Experimental results showed that our approach could accurately determine wheelchair maneuvers regardless of the models and placements of the mobile devices.

Associations of SRD5A2/CYP17/CYP19/VDR gene polymorphisms with the development and clinical progression of benign prostatic hyperplasia: a case-control study in northern Chinese population.

This study aims to explore the effect of gene polymorphisms of 5a-reduction enzyme (SRD5A2), steroidogenic cytochrome P-450 17alpha-hydroxylase (CYP17), aromatase cytochrome P450 family 19 (CYP19) and vita-min D receptor (VDR) on benign prostatic hyperplasia (BPH) susceptibility and clinical progress. A total of 452 BHP patients and 501 healthy individuals were selected in Harbin Medical University Daqing School from October 2014 and December 2015 as the case and control groups. All BPH patients received drug treatment and were subsequently divided into the progression and non-progression groups based on their therapeutic efficacy. PCR-RFLP was applied to detect the genotype distributions of SRD5A2/CYP17/CYP19/VDR, which were further tested with Hardy-Weinberg (H-W) equilibrium. Logistic regression analysis was applied to determine the risk factors for BPH progression. Compared with subjects carrying VV genotype and V allele at SRD5A2 V89L, those with LL genotype and L allele at SRD5A2 V89L may have reduced risk of BPH susceptibility or progression (all P < 0.05). Compared with subjects carrying TT genotype and T allele at CYP17 -34T>C, those with CC genotype and C allele at CYP17 -34T>C may have increased risk of BPH susceptibility or progression (all P < 0.05). Compared with individuals carrying FF genotype and F allele at VDRVDR Fok I, those with ff genotype and f allele at VDRVDR Fok I may have increased susceptibility to BPH (all P < 0.05). Logistic regression analysis showed that SRD5A2 V89L and CYP17 -34T>C polymorphisms and CYP17 -34T>C (TC + CC)/SRD5A2 V89L (VV) combined genotypes were significantly related with the clinical progression of BHP. These results revealed that SRD5A2 V89L and CYP17 -34T>C polymorphisms were associated with the risk of BPH and its clinical progression.

A novel mobile-cloud system for capturing and analyzing wheelchair maneuvering data: A pilot study.

The purpose of this pilot study was to provide a new approach for capturing and analyzing wheelchair maneuvering data, which are critical for evaluating wheelchair users' activity levels. We proposed a mobile-cloud (MC) system, which incorporated the emerging mobile and cloud computing technologies. The MC system employed smartphone sensors to collect wheelchair maneuvering data and transmit them to the cloud for storage and analysis. A k-nearest neighbor (KNN) machine-learning algorithm was developed to mitigate the impact of sensor noise and recognize wheelchair maneuvering patterns. We conducted 30 trials in an indoor setting, where each trial contained 10 bouts (i.e., periods of continuous wheelchair movement). We also verified our approach in a different building. Different from existing approaches that require sensors to be attached to wheelchairs' wheels, we placed the smartphone into a smartphone holder attached to the wheelchair. Experimental results illustrate that our approach correctly identified all 300 bouts. Compared to existing approaches, our approach was easier to use while achieving similar accuracy in analyzing the accumulated movement time and maximum period of continuous movement (p > 0.8). Overall, the MC system provided a feasible way to ease the data collection process and generated accurate analysis results for evaluating activity levels.

Development of intelligent model for personalized guidance on wheelchair tilt and recline usage for people with spinal cord injury: methodology and preliminary report.

Wheelchair tilt and recline functions are two of the most desirable features for relieving seating pressure to decrease the risk of pressure ulcers. The effective guidance on wheelchair tilt and recline usage is therefore critical to pressure ulcer prevention. The aim of this study was to demonstrate the feasibility of using machine learning techniques to construct an intelligent model to provide personalized guidance to individuals with spinal cord injury (SCI). The motivation stems from the clinical evidence that the requirements of individuals vary greatly and that no universal guidance on tilt and recline usage could possibly satisfy all individuals with SCI. We explored all aspects involved in constructing the intelligent model and proposed approaches tailored to suit the characteristics of this preliminary study, such as the way of modeling research participants, using machine learning techniques to construct the intelligent model, and evaluating the performance of the intelligent model. We further improved the intelligent model's prediction accuracy by developing a two-phase feature selection algorithm to identify important attributes. Experimental results demonstrated that our approaches held the promise: they could effectively construct the intelligent model, evaluate its performance, and refine the participant model so that the intelligent model's prediction accuracy was significantly improved.

rs10505474 and rs7837328 at 8q24 cumulatively confer risk of prostate cancer in Northern Han Chinese.

AIMS: Genome-wide association studies (GWAS) have identified several risk variants for prostate cancer (pCa) mainly in Europeans, which need to be further verified in other racial groups. We selected six previously identified variants as candidates and to define the association with PCa in Northern Han Chinese. METHODS: 749 subjects from Beijing and Tianjin in Northern China were included. Six variants (rs10505474, rs7837328, rs4242384, rs7813, rs486907 and rs1058205) were genotyped by high resolution melting (HRM) assays. The individual and cumulative contribution for of the risk of PCa and clinical covariates were analyzed. RESULTS: Among the six candidate variants, only rs10505474, and rs7837328, both locating at 8q24 region, were associated with PCa in our population.rs10505474 (A) was associated with PCa (ORrecessive= 1.56, p=0.006); and rs7837328 (A) was associated with PCa (ORdominant= 1.38, p=0.042/ORrecessive=1.99, p=0.003). Moreover, we observed a cumulative effects between them (ptrend=2.58?10-5). The joint population attributable risk showed the two variants might account for 71.85% of PCa risk. In addition, we found the homozygotes of rs10505474 (A) and rs7837328 (A) were associated with PCa clinical covariants (age at onset, tumor stage, respectively) (page=0.046, Ptumorstage =0.048). CONCLUSION: rs10505474 (A) and rs7387328 (A) at 8q24 are associated with PCa and cumulatively confer risk, suggesting the two variations could determine susceptibility to PCa in the Northern Chinese Han population.

Characterization of wheelchair maneuvers based on noisy inertial sensor data: a preliminary study.

A wheelchair user's activity and mobility level is an important indicator of his/her quality of life and health status. To assess the activity and mobility level, wheelchair maneuvering data must be captured and analyzed. Recently, the inertial sensors, such as accelerometers, have been used to collect wheelchair maneuvering data. However, these sensors are sensitive to noises, which can lead to inaccurate analysis results. In this study, we analyzed the characteristics of wheelchair maneuvering data and developed a novel machine-learning algorithm, which could classify wheelchair maneuvering data into a series of wheelchair maneuvers. The use of machine-learning techniques empowers our approach to tolerate noises by capturing the patterns of wheelchair maneuvers. Experimental results showed that the proposed algorithm could accurately classify wheelchair maneuvers into eight classes, i.e., stationary, linear acceleration/deceleration, linear constant speed, left/right turns, and left/right spot turns. The fine-grained analysis on wheelchair maneuvering data can depict a more comprehensive picture of wheelchair users' activity and mobility levels, and enable the quantitative analysis of their quality of life and health status.

Capturing and analyzing wheelchair maneuvering patterns with mobile cloud computing.

Power wheelchairs have been widely used to provide independent mobility to people with disabilities. Despite great advancements in power wheelchair technology, research shows that wheelchair related accidents occur frequently. To ensure safe maneuverability, capturing wheelchair maneuvering patterns is fundamental to enable other research, such as safe robotic assistance for wheelchair users. In this study, we propose to record, store, and analyze wheelchair maneuvering data by means of mobile cloud computing. Specifically, the accelerometer and gyroscope sensors in smart phones are used to record wheelchair maneuvering data in real-time. Then, the recorded data are periodically transmitted to the cloud for storage and analysis. The analyzed results are then made available to various types of users, such as mobile phone users, traditional desktop users, etc. The combination of mobile computing and cloud computing leverages the advantages of both techniques and extends the smart phone's capabilities of computing and data storage via the Internet. We performed a case study to implement the mobile cloud computing framework using Android smart phones and Google App Engine, a popular cloud computing platform. Experimental results demonstrated the feasibility of the proposed mobile cloud computing framework.

Towards an intelligent system for clinical guidance on wheelchair tilt and recline usage.

We propose to construct an intelligent system for clinical guidance on how to effectively use power wheelchair tilt and recline functions. The motivations fall into the following two aspects. (1) People with spinal cord injury (SCI) are vulnerable to pressure ulcers. SCI can lead to structural and functional changes below the injury level that may predispose individuals to tissue breakdown. As a result, pressure ulcers can significantly affect the quality of life, including pain, infection, altered body image, and even mortality. (2) Clinically, wheelchair power seat function, i.e., tilt and recline, is recommended for relieving sitting-induced pressures. The goal is to increase skin blood flow for the ischemic soft tissues to avoid irreversible damage. Due to variations in the level and completeness of SCI, the effectiveness of using wheelchair tilt and recline to reduce pressure ulcer risks has considerable room for improvement. Our previous study indicated that the blood flow of people with SCI may respond very differently to wheelchair tilt and recline settings. In this study, we propose to use the artificial neural network (ANN) to predict how wheelchair power seat functions affect blood flow response to seating pressure. This is regression learning because the predicted outputs are numerical values. Besides the challenging nature of regression learning, ANN may suffer from the overfitting problem which, when occurring, leads to poor predictive quality (i.e., cannot generalize). We propose using the particle swarm optimization (PSO) algorithm to train ANN to mitigate the impact of overfitting so that ANN can make correct predictions on both existing and new data. Experimental results show that the proposed approach is promising to improve ANN's predictive quality for new data.

Development of intelligent model to determine favorable wheelchair tilt and recline angles for people with spinal cord injury.

Machine-learning techniques have found widespread applications in bioinformatics. Such techniques provide invaluable insight on understanding the complex biomedical mechanisms and predicting the optimal individualized intervention for patients. In our case, we are particularly interested in developing an individualized clinical guideline on wheelchair tilt and recline usage for people with spinal cord injury (SCI). The current clinical practice suggests uniform settings to all patients. However, our previous study revealed that the response of skin blood flow to wheelchair tilt and recline settings varied largely among patients. Our finding suggests that an individualized setting is needed for people with SCI to maximally utilize the residual neurological function to reduce pressure ulcer risk. In order to achieve this goal, we intend to develop an intelligent model to determine the favorable wheelchair usage to reduce pressure ulcers risk for wheelchair users with SCI. In this study, we use artificial neural networks (ANNs) to construct an intelligent model that can predict whether a given tilt and recline setting will be favorable to people with SCI based on neurological functions and SCI injury history. Our results indicate that the intelligent model significantly outperforms the traditional statistical approach in accurately classifying favorable wheelchair tilt and recline settings. To the best of our knowledge, this is the first study using intelligent models to predict the favorable wheelchair tilt and recline angles. Our methods demonstrate the feasibility of using ANN to develop individualized wheelchair tilt and recline guidance for people with SCI.