The development of risk assessment models for carpal tunnel syndrome: a case-referent study

The present study developed risk assessment models for carpal tunnel syndrome (CTS) which can provide information of the likelihood of developing CTS for an individual having certain personal characteristics and occupational risks. A case-referent study was conducted consisting of two case groups and one referent group: (1) 22 work-related CTS patients (W-CTS), (2) 25 non-work related CTS patients (NW-CTS), and (3) 50 healthy workers (HEALTHY) having had no CTS history. The classification of CTS patients into one of the case groups was determined according to the type of insurance covering their medical costs. Personal characteristics, psychosocial stresses at work, and physical work conditions were surveyed by using a questionnaire tailor-designed to CTS (reliability of each scale > or = 0.7). By contrasting the risk information of each case group to that of the referent group, three logistic regression models were developed: W-CTS/HEALTHY, NW-CTS/HEALTHY, and C-CTS/HEALTHY (C-CTS, the combined group of W-CTS and NW-CTS). ROC analysis indicated that the models have satisfactory discriminability (d' = 1.91 to 2.51) and high classification accuracy (overall accuracy = 83-89%). Both W-CTS/HEALTHY and C-CTS/HEALTHY include personal and physical factors, while NW-CTS/HEALTHY involves only personal factors. This suggests that the injury causation of NW-CTS patients should be attributable mainly to their 'high' personal susceptibility to the disorder rather than exposure to adverse work conditions, while that of W-CTS patients be attributable to improper work conditions and CTS-prone personal characteristics in combination.     

Gender differences in prevalence of upper extremity musculoskeletal disorders

A literature review was conducted on the current literature base to determine the strength of support for the hypothesis that women experience higher prevalences of upper extremity musculoskeletal disorders (UEMSDs) than men. Fifty-six articles were reviewed for data on gender differences in frequency, incidence or prevalence rates for musculoskeletal disorders specific to the upper extremities. These articles included both general and working populations. The majority of the studies showed that women had significantly higher incidences of various types of UEMSDs than men. This trend was consistently observed in studies based on self-report or plant/workers compensation records, with and without physical examination, and after adjusting for potential confounders such as age and physical work factors. With men as the referent, the odds ratio (OR) or prevalence ratio (PR) for UEMSDs ranged from 0.85 to 10.05 for self report. For self report combined with physical examination, the OR/PR ranged from 0.66 to 11.4. The OR/PR for carpal tunnel syndrome (CTS) ranged from 0.6 to 2.87 with confounder adjustment. These findings suggest that women do have significantly higher prevalences than men for many types of UEMSDs, even after controlling for the type of data source and confounders such as age or work factors.     

Gender differences in prevalence of upper extremity musculoskeletal disorders

A literature review was conducted on the current literature base to determine the strength of support for the hypothesis that women experience higher prevalences of upper extremity musculoskeletal disorders (UEMSDs) than men. Fifty-six articles were reviewed for data on gender differences in frequency, incidence or prevalence rates for musculoskeletal disorders specific to the upper extremities. These articles included both general and working populations. The majority of the studies showed that women had significantly higher incidences of various types of UEMSDs than men. This trend was consistently observed in studies based on self-report or plant/workers compensation records, with and without physical examination, and after adjusting for potential confounders such as age and physical work factors. With men as the referent, the odds ratio (OR) or prevalence ratio (PR) for UEMSDs ranged from 0.85 to 10.05 for self report. For self report combined with physical examination, the OR/PR ranged from 0.66 to 11.4. The OR/PR for carpal tunnel syndrome (CTS) ranged from 0.6 to 2.87 with confounder adjustment. These findings suggest that women do have significantly higher prevalences than men for many types of UEMSDs, even after controlling for the type of data source and confounders such as age or work factors.     

一种改进的案例推理分类方法研究

特征属性的权重分配和案例检索策略对案例推理（Case-based reasoning,CBR）分类的准确率有显著影响. 本文提出一种结合遗传算法、内省学习和群决策思想改进的CBR分类方法. 首先,利用遗传算法得到多组属性权重,再根据内省学习原理对每组权重进行迭代调整;然后,通过案例群检索策略得到满足大多数原则的群决策分类结果;最后,以典型分类数据集的对比实验证明了本文方法能进一步提高CBR分类的准确率. 这表明内省学习可以保证权重分配的合理性,案例群检索策略能充分利用案例库的潜在信息,对提升CBR的学习能力有显著作用.     

The use of continuous exposure data for predicting CTS in fish processing operators

Carpal tunnel syndrome (CTS) remains one of the most commonly reported and studied work related musculoskeletal disorders. Categorical representations of exposures has been critical in identifying associations between risk factors and CTS, however, quantification of exposure—response relationships require using continuous exposure data. Also, few interactions between risk factors, especially between risk factor categories, have been investigated. The objectives of this study were to investigate the utility of using continuous exposure data and to identify interaction effects of risk factors, both within and between risk factor categories, for predicting CTS. A cross sectional study was performed at a fish processing facility in which 53 participants were evaluated during normal task performance. Due to task asymmetry, each hand was considered separately, providing 106 hands for analysis. Direct measurement and a questionnaire were used to quantify exposures to common occupational and personal risk factors. Stepwise logistic regression analysis was performed to identify three models for predicting CTS and assess predictive ability using: occupational risk factors only (three-way interactions considered), personal risk factors only (two-way interactions considered), and a mixed model considering two-way interactions across risk factor categories and previously identified significant interactions. Models including only occupational or personal risk factors were moderately accurate overall (73% and 77% respectively), but were not sensitive in differentiating between CTS cases and non-cases (39% and 33% respectively). The mixed model was found to be accurate (88%) and sensitive (78%), though only one interaction effect was included. The results of this study illustrate the importance of using continuous exposure data, especially in job tasks where exposures to occupational risk factors is similar, when differentiating between high and low risk job tasks.     

Functional psychomotor deficits associated with carpal tunnel syndrome

A reliable task was developed for investigating functional deficits associated with carpal tunnel syndrome (CTS). A rapid pinch and release psychomotor task utilizing muscles of the hand innervated by the median nerve was administered using a strain gauge dynamometer and providing limited force feedback. The motor performance characteristics studied were speed and force control. An experiment was conducted for studying the effects of force level, hand dominance, test-retest reliability, learning, and inter-subject variability using 13 subjects free from any hand disabilities or symptoms. A companion study was also conducted using 17 normal subjects and ten subjects diagnosed having CTS to investigate differences between CTS and control subjects. Dominant hands performed 4% to 8% better than the non-dominant hands by having a greater pinch rate, a smaller overshoot force, and less time above the upper force level and below the lower force level. Control subjects performed 25% to 82% better than CTS subjects. Age contributed 6% of the total variance for pinch rate and 7% of the total variance for the time below the lower force level. The results suggest that people suffering from CTS may experience similar functional psychomotor deficits in daily living and manual work activities.     

The use of continuous exposure data for predicting CTS in fish processing operators

Carpal tunnel syndrome (CTS) remains one of the most commonly reported and studied work related musculoskeletal disorders. Categorical representations of exposures has been critical in identifying associations between risk factors and CTS, however, quantification of exposure-response relationships require using continuous exposure data. Also, few interactions between risk factors, especially between risk factor categories, have been investigated. The objectives of this study were to investigate the utility of using continuous exposure data and to identify interaction effects of risk factors, both within and between risk factor categories, for predicting CTS. A cross sectional study was performed at a fish processing facility in which 53 participants were evaluated during normal task performance. Due to task asymmetry, each hand was considered separately, providing 106 hands for analysis. Direct measurement and a questionnaire were used to quantify exposures to common occupational and personal risk factors. Stepwise logistic regression analysis was performed to identify three models for predicting CTS and assess predictive ability using: occupational risk factors only (three-way interactions considered), personal risk factors only (two-way interactions considered), and a mixed model considering two-way interactions across risk factor categories and previously identified significant interactions. Models including only occupational or personal risk factors were moderately accurate overall (73% and 77% respectively), but were not sensitive in differentiating between CTS cases and non-cases (39% and 33% respectively). The mixed model was found to be accurate (88%) and sensitive (78%), though only one interaction effect was included. The results of this study illustrate the importance of using continuous exposure data, especially in job tasks where exposures to occupational risk factors is similar, when differentiating between high and low risk job tasks.     

Investigating the Role of a Large,Shared Display in Multi-Display Environments

We conducted an empirical study to investigate the use of personal and shared displays during group work. The collaborative environments under study consisted of personal workspaces, in the form of laptops, and a shared virtual workspace displayed on a nearby wall. Our study compared the use of the large shared display under two different interface content conditions; a status display that provided an overview of the group’s current task performance, and a replicated view of the shared workspace that allowed task work to occur on the shared display. The study results suggest that while participants used their personal displays primarily to perform the task, the shared display facilitated several key teamwork mechanisms. In particular, the provided status display best facilitated monitoring of group progress, whereas the replicated content display best facilitated conversational grounding. Regardless of the shared display content, having a shared, physical reference point also appeared to support synchronization of the group activity via body language and gaze.     

Tolerating Concept and Sampling Shift in Lazy Learning Using Prediction Error Context Switching

In their unmodified form, lazy-learning algorithms may have difficulty learning and tracking time-varying input/output function maps such as those that occur in concept shift. Extensions of these algorithms, such as Time-Windowed forgetting (TWF), can permit learning of time-varying mappings by deleting older exemplars, but have decreased classification accuracy when the input-space sampling distribution of the learning set is time-varying. Additionally, TWF suffers from lower asymptotic classification accuracy than equivalent non-forgetting algorithms when the input sampling distributions are stationary. Other shift-sensitive algorithms, such as Locally-Weighted forgetting (LWF) avoid the negative effects of time-varying sampling distributions, but still have lower asymptotic classification in non-varying cases. We introduce Prediction Error Context Switching (PECS) which allows lazy-learning algorithms to have good classification accuracy in conditions having a time-varying function mapping and input sampling distributions, while still maintaining their asymptotic classification accuracy in static tasks. PECS works by selecting and re-activating previously stored instances based on their most recent consistency record. The classification accuracy and active learning set sizes for the above algorithms are compared in a set of learning tasks that illustrate the differing time-varying conditions described above. The results show that the PECS algorithm has the best overall classification accuracy over these differing time-varying conditions, while still having asymptotic classification accuracy competitive with unmodified lazy-learners intended for static environments.     

Complex models for animating synthetic actors

A classification of models for animating synthetic actors is proposed. The classification depends on the motion control method used (geometric, physical, or behavioral) and the type of actor interaction (single actor, actor-actor, actor-environment, and animator-actor). These interactions and models give rise to a classification array with four rows and three columns. Each case represents existing and potential approaches to animation control. The characteristics of each motion control method and interaction type are examined     

Comparing the results from various performance models of IEEE 802.11g DCF

IEEE 802.11 DCF is the most widely-used CSMA/CA access control mechanism. Recent analytic performance models for DCF have received acclaim for both their simplicity and reported accuracy. Most of these models share the assumptions of full single-hop connectivity among all stations, that DCF back-off may be modeled as a Markov process and that the network is saturated with traffic. In order to verify the accuracy of existing analytic models we developed a discrete-event simulator to record the performance of the DCF protocol and ensure that every detail of the standard is represented. Simultaneously we set up a hardware test bed to measure the same performance metrics in an environment that makes none of the simplifying assumptions of either the analytic models or the simulation. In the test bed, as in the simulator, we used the same physical parameter settings prescribed by the standard. As is the case for the analytic models we used, we subjected the simulator and the test bed to the same saturated workload for both basic and RTS/CTS access modes. Finally, we also implemented a non-saturating Markov Modulated Arrival Process (MMAP) workload model for our simulator to test the performance of DCF subject to more realistic internet traffic conditions. We describe both the simulator and the test bed in some detail in order to testify to the accuracy and detail of our results. The results show that the analytic models are mostly pessimistic for small numbers of nodes and optimistic for larger numbers of nodes. The performance measurements from the test bed, in turn, indicate that the simulation results are similarly optimistic when large numbers of nodes are concerned. Since the test bed uses an error-prone wireless channel, this latter result is, in principle, not surprising. The rate of deterioration in the actual performance is however something that is not widely known and is much more rapid than analytic models would suggest.     

Cascaded model adaptation for dialog act segmentation and tagging

There are many speech and language processing problems which require cascaded classification tasks. While model adaptation has been shown to be useful in isolated speech and language processing tasks, it is not clear what constitutes system adaptation for such complex systems. This paper studies the following questions: In cases where a sequence of classification tasks is employed, how important is to adapt the earlier or latter systems? Is the performance improvement obtained in the earlier stages via adaptation carried on to later stages in cases where the later stages perform adaptation using similar data and/or methods? In this study, as part of a larger scale multiparty meeting understanding system, we analyze various methods for adapting dialog act segmentation and tagging models trained on conversational telephone speech (CTS) to meeting style conversations. We investigate the effect of using adapted and unadapted models for dialog act segmentation with those of tagging, showing the effect of model adaptation for cascaded classification tasks. Our results indicate that we can achieve significantly better dialog act segmentation and tagging by adapting the out-of-domain models, especially when the amount of in-domain data is limited. Experimental results show that it is more effective to adapt the models in the latter classification tasks, in our case dialog act tagging, when dealing with a sequence of cascaded classification tasks.     

Combining a new data classification technique and regression analysis to predict the Cost-To-Serve new customers

Identifying the Cost-To-Serve (CTS) of customers is one of the most challenging problems in Supply Chain Management because of the diversity in their business activities. For the particular case of the industrial gas business, we are interested in predicting the cost to deliver bulk (liquefied) gas to new customers using a multifactor linear regression model. Developing a single model, i.e. analyzing the observations all at once, produces poor prediction results. Therefore prior to the regression analysis, a new supervised learning technique is used to group customers who are similar in some sense. Classes of customers are represented by hyper-boxes and a linear regression model is subsequently built within each class. The combination of data classification and regression is proven to increase the accuracy of the prediction.Two Mixed-Integer-Linear Programming (MILP) models are developed for data classification purposes. Although we are dealing with a supervised learning method, classes are not predefined in our case. Rather, we input a continuous “classification” attribute that is optimally discretized by the MILP’s in order to minimize the number of misclassifications. Therefore our data classification model offers a broader range of applications. A number of illustrative examples are used to prove the effectiveness of the proposed approach.     

Costs of occupational musculoskeletal disorders (MSDs) in the United States

Background

For the years 1992–2010 musculoskeletal disorders (MSDs) accounted for 29–35% of all occupational injuries and illnesses involving days away from work in the United States (US) (AFL-CIO, 2012). According to the American Federation of Labor and Congress of Industrial Organizations (AFL-CIO) 2012 report ‘Death on the Job’, for the years 1992 through 2010 the percent of cases involving MSDs in private industry were highest in 2000 (35%) and lowest in 2007 (29%). In 2010, the median number of days away from work for MSDs was 11 compared to 8 for all occupational injury cases involving days away from work; the median number of days away from work for Carpal Tunnel Syndrome (CTS) was 25, more than three times as high as for all other BLS injuries involving days away from work (BLS, 2011). This study estimated the costs of work related MSDs, and given that the number of days lost due to CTS is very high, it also estimated the costs of CTS separately in the United States (US) for the years 2003 through 2007.

Methods

The costs of work related MSDs and CTS in the US were estimated using the cost-of-illness, human capital method (Leigh et al., 2000), using some of the costs from the literature. This method decomposes costs into direct and indirect categories. Estimates of total cost of MSDs and CTS were obtained from the product of average costs of MSDs and CTS and the number of MSDs and CTS. The number of MSDs and CTS were obtained from BLS data.

Results

The number of reported work-related MSDs declined from 435,180 in 2003 to 335,390 in 2007 and the reported number of CTS also declined from 22,110 in 2003 to 11,920 in 2007. The direct costs of MSDs and CTS were respectively $1.5 billion and $0.1 billion for the year 2007. The indirect costs were $1.1 billion and $0.1 billion for MSDs and CTS respectively for the year 2007.

Discussion

This study found that the total costs of work-related MSDs and CTS declined during the period 2003 through 2007 but the average costs per case went up signifying that medical costs and other associated costs increased during this period.

Relevance to industry

The costs of MSDs are important to the industries too as a significant part of these costs are borne by the employers. Industries with higher prevalence of MSDs are affected more in terms of lost productivities due to the employees' days away from work because of MSDs. In cases of MSDs causing permanent disabilities, new hiring and training costs are also a part of the losses experienced by the employers.     

Using a smart textile system for classifying occupational manual material handling tasks: evidence from lab-based simulations

Physical monitoring systems represent potentially powerful assessment devices to detect and describe occupational physical activities. A promising technology for such use is smart textile systems (STSs). Our goal in this exploratory study was to assess the feasibility and accuracy of using two STSs to classify several manual material handling (MMH) tasks. Specifically, commercially-available ‘smart’ socks and a custom ‘smart’ shirt were used individually and in combination. Eleven participants simulated nine separate MMH tasks while wearing the STSs, and task classification accuracy was quantified subsequently using several common models. The shirt and socks, both individually and in combination, could classify the simulated tasks with greater than 97% accuracy. Thus, using STSs appears to have potential utility for discriminating occupational physical tasks in the work environment.

Practitioner summary: A smart textile system could classify diverse MMH tasks with high accuracy. This technology may help in developing future ergonomic exposure assessment systems, with the goal of preventing occupational injuries.     

Understanding people motion in video sequences using Voronoi diagrams

This work describes a model for understanding people motion in video sequences using Voronoi diagrams, focusing on group detection and classification. We use the position of each individual as a site for the Voronoi diagram at each frame, and determine the temporal evolution of some sociological and psychological parameters, such as distance to neighbors and personal spaces. These parameters are used to compute individual characteristics (such as perceived personal space and comfort levels), that are analyzed to detect the formation of groups and their classification as voluntary or involuntary. Experimental results based on videos obtained from real life as well as from a crowd simulator were analyzed and discussed.     

Verification and analysis of domain-specific models of physical characteristics in embedded control software

ContextA considerable portion of the software systems today are adopted in the embedded control domain. Embedded control software deals with controlling a physical system, and as such models of physical characteristics become part of the embedded control software.ObjectiveDue to the evolution of system properties and increasing complexity, faults can be left undetected in these models of physical characteristics. Therefore, their accuracy must be verified at runtime. Traditional runtime verification techniques that are based on states/events in software execution are inadequate in this case. The behavior suggested by models of physical characteristics cannot be mapped to behavioral properties of software. Moreover, implementation in a general-purpose programming language makes these models hard to locate and verify. Therefore, this paper proposes a novel approach to perform runtime verification of models of physical characteristics in embedded control software.MethodThe development of an approach for runtime verification of models of physical characteristics and the application of the approach to two industrial case studies from the printing systems domain.ResultsThis paper presents a novel approach to specify models of physical characteristics using a domain-specific language, to define monitors that detect inconsistencies by exploiting redundancy in these models, and to realize these monitors using an aspect-oriented approach. We complement runtime verification with static analysis to verify the composition of domain-specific models with the control software written in a general-purpose language.ConclusionsThe presented approach enables runtime verification of implemented models of physical characteristics to detect inconsistencies in these models, as well as broken hardware components and wear and tear of hardware in the physical system. The application of declarative aspect-oriented techniques to realize runtime verification monitors increases modularity and provides the ability to statically verify this realization. The complementary static and runtime verification techniques increase the reliability of embedded control software.     

AVHRR composite period selection for land cover classification

Multitemporal satellite image datasets provide valuable information on the phenological characteristics of vegetation, thereby significantly increasing the accuracy of cover type classifications compared to single date classifications. However, the processing of these datasets can become very complex when dealing with multitemporal data combined with multispectral data. Advanced Very High Resolution Radiometer (AVHRR) biweekly composite data are commonly used to classify land cover over large regions. Selecting a subset of these biweekly composite periods may be required to reduce the complexity and cost of land cover mapping. The objective of our research was to evaluate the effect of reducing the number of composite periods and altering the spacing of those composite periods on classification accuracy. Because inter-annual variability can have a major impact on classification results, 5 years of AVHRR data were evaluated. AVHRR biweekly composite images for spectral channels 1-4 (visible, nearinfrared and two thermal bands) covering the entire growing season were used to classify 14 cover types over the entire state of Colorado for each of five different years. A supervised classification method was applied to maintain consistent procedures for each case tested. Results indicate that the number of composite periods can be halved-reduced from 14 composite dates to seven composite dates-without significantly reducing overall classification accuracy (80.4% Kappa accuracy for the 14-composite dataset as compared to 80.0% for a seven-composite dataset). At least seven composite periods were required to ensure the classification accuracy was not affected by inter-annual variability due to climate fluctuations. Concentrating more composites near the beginning and end of the growing season, as compared to using evenly spaced time periods, consistently produced slightly higher classification values over the 5 years tested (average Kappa of 80.3% for the heavy early/late case as compared to 79.0% for the alternate dataset case).     

A high performance hybrid SSVEP based BCI speller system

The existing EEG based keyboard/speller systems have a tradeoff between the target detection time and classification accuracy. This study focuses on increasing the accuracy and probability of target classification rates in the SSVEP based speller system. We proposed two different types of hybrid SSVEP system by combining SSVEP with vision based eye gaze tracker (VET) and electro-oculogram (EOG). Thirty six targets were randomly chosen for this study and their corresponding visual stimulus was presented with unique frequencies. The visual stimuli were segregated into three groups and each group were arranged into different regions (left/middle/right) of the keyboard/speller layout for improving the probability of target detection rate. The VET/ EOG data were utilized to identify the regions that belong to the selected target. The region/group determination decreases the issue of misclassification of SSVEP frequencies. The averaged spelling accuracies of SSVEP-VET and SSVEP-EOG system for all the subjects is 91.2% and 91.39% respectively. Later, a visual feedback was added to the SSVEP-EOG system (SSVEP-EOG-VF) for improving the target detection rate. In this case, an average classification accuracy of 98.33% was obtained with the information transfer rate (ITR) of 69.21 bits/min for all the subjects. An accuracy of 100% was obtained for five subjects with the ITR of 74.1 bits/min in this system.     

5G环境下车联网跟驰模型

针对现有跟驰模型的不足,提出一种5G环境下车联网实时稳定(current time stabilization,CTS)模型。该跟驰模型首先考虑了网络时延的时变性对跟驰模型的影响,其次考虑了由网络时延带来的对车辆实时速度、加速度、驾驶员反应时间的影响,从而保证CTS模型更符合实际驾驶情况。从理论上推导出跟驰模型的临界稳定性条件。仿真结果表明,当网络时延不同时,随着网络时延的降低,CTS模型的稳定性不断升高;在相同扰动下,CTS模型比传统优化速度(optimal velocity,OV)跟驰模型的稳定性提高了32.5%。