10 research outputs found
Gait Velocity Estimation using time interleaved between Consecutive Passive IR Sensor Activations
Gait velocity has been consistently shown to be an important indicator and
predictor of health status, especially in older adults. It is often assessed
clinically, but the assessments occur infrequently and do not allow optimal
detection of key health changes when they occur. In this paper, we show that
the time gap between activations of a pair of Passive Infrared (PIR) motion
sensors installed in the consecutively visited room pair carry rich latent
information about a person's gait velocity. We name this time gap transition
time and show that despite a six second refractory period of the PIR sensors,
transition time can be used to obtain an accurate representation of gait
velocity.
Using a Support Vector Regression (SVR) approach to model the relationship
between transition time and gait velocity, we show that gait velocity can be
estimated with an average error less than 2.5 cm/sec. This is demonstrated with
data collected over a 5 year period from 74 older adults monitored in their own
homes.
This method is simple and cost effective and has advantages over competing
approaches such as: obtaining 20 to 100x more gait velocity measurements per
day and offering the fusion of location-specific information with time stamped
gait estimates. These advantages allow stable estimates of gait parameters
(maximum or average speed, variability) at shorter time scales than current
approaches. This also provides a pervasive in-home method for context-aware
gait velocity sensing that allows for monitoring of gait trajectories in space
and time
Estimating the chance of success in IVF treatment using a ranking algorithm
In medicine, estimating the chance of success for treatment is important in deciding whether to begin the treatment or not. This paper focuses on the domain of in vitro fertilization (IVF), where estimating the outcome of a treatment is very crucial in the decision to proceed with treatment for both the clinicians and the infertile couples. IVF treatment is a stressful and costly process. It is very stressful for couples who want to have a baby. If an initial evaluation indicates a low pregnancy rate, decision of the couple may change not to start the IVF treatment. The aim of this study is twofold, firstly, to develop a technique that can be used to estimate the chance of success for a couple who wants to have a baby and secondly, to determine the attributes and their particular values affecting the outcome in IVF treatment. We propose a new technique, called success estimation using a ranking algorithm (SERA), for estimating the success of a treatment using a ranking-based algorithm. The particular ranking algorithm used here is RIMARC. The performance of the new algorithm is compared with two well-known algorithms that assign class probabilities to query instances. The algorithms used in the comparison are Naïve Bayes Classifier and Random Forest. The comparison is done in terms of area under the ROC curve, accuracy and execution time, using tenfold stratified cross-validation. The results indicate that the proposed SERA algorithm has a potential to be used successfully to estimate the probability of success in medical treatment. © 2015, The Author(s)
Applications of hybrid dynamic Bayesian networks to water reservoir management
Bayesian networks (BNs) have been widely applied in environmental modelling to predict the behaviour of an ecosystem under conditions of change. However, this approximation doesn’t take time into consideration. To solve this issue, an extension of BNs, the dynamic Bayesian networks (DBNs), has been developed in mathematics and computer science areas but has scarcely been applied in environmental modelling. This paper presents the application of DBN to water reservoir systems in Andalusia, Spain. The aim is to predict changes in the percent fullness of the reservoirs under the irregular rainfall patterns of Mediterranean watersheds. In comparison to static BNs, DBNs provide results that can be extrapolated to a particular time so that a climate change scenario can be studied in detail over time. Since results are expressed by density functions rather than unique values, several metrics are obtained from the results, including the probability of certain values. This allows the probability that water level in a reservoir reaches a certain level to be directly computed
Evidence of Temporal Bayesian Networks applications for health-related problems: a systematic review
A dynamic bayesian network for estimating the risk of falls from real gait data
Abstract Pathological and age-related changes may affect an individual's gait, in turn raising the risk of falls. In elderly, falls are common and may eventuate in severe injuries, long-term disabilities, and even death. Thus, there is interest in estimating the risk of falls from gait analysis. Estimation of the risk of falls requires consideration of the longitudinal evolution of different variables derived from human gait. Bayesian networks are probabilistic models which graphically express dependencies among variables. Dynamic Bayesian networks (DBNs) are a type of BN adequate for modeling the dynamics of the statistical dependencies in a set of variables. In this work, a DBN model incorporates gait derived variables to predict the risk of falls in elderly within 6 months subsequent to gait assessment. Two DBNs were developed; the first (DBN1; expert-guided) was built using gait variables identified by domain experts, whereas the second (DBN2; strictly computational) was constructed utilizing gait variables picked out by a feature selection algorithm. The effectiveness of the second model to predict falls in the 6 months following assessment is 72.22 %. These results are encouraging and supply evidence regarding the usefulness of dynamic probabilistic models in the prediction of falls from pathological gait
Probabilistic analysis of supply chains resilience based on their characteristics using dynamic Bayesian networks
Previously held under moratorium from 14 December 2016 until 19 January 2022There is an increasing interest in the resilience of supply chains given the growing
awareness of their vulnerabilities to natural and man-made hazards. Contemporary
academic literature considers, for example, so-called resilience enablers and strategies,
such as improving the nature of collaboration and flexibility within the supply chain.
Efforts to analyse resilience tend to view the supply chain as a complex system. The
present research adopts a distinctive approach to the analysis of supply resilience by
building formal models from the perspective of the responsible manager. Dynamic
Bayesian Networks (DBNs) are selected as the modelling method since they are capable
of representing the temporal evolution of uncertainties affecting supply. They also
support probabilistic analysis to estimate the impact of potentially hazardous events
through time. In this way, the recovery rate of the supply chain under mitigation action
scenarios and an understanding of resilience can be obtained.
The research is grounded in multiple case studies of manufacturing and retail supply
chains, involving focal companies in the UK, Canada and Malaysia, respectively. Each
case involves building models to estimate the resilience of the supply chain given
uncertainties about, for example, business continuity, lumpy spare parts demand and
operations of critical infrastructure. DBNs have been developed by using relevant data
from historical empirical records and subjective judgement. Through the modelling
practice, It has been found that some SC characteristics (i.e. level of integration,
structure, SC operating system) play a vital role in shaping and quantifying DBNs and
reduce their elicitation burden. Similarly, It has been found that the static and dynamic
discretization methods of continuous variables affect the DBNs building process.
I also studied the effect of level of integration, visibility, structure and SC operating
system on the resilience level of SCs through the analysis of DBNs outputs. I found that
the influence of the integration intensity on supply chain resilience can be revealed
through understanding the dependency level of the focal firm on SC members resources. I
have also noticed the relationship between the span of integration and the level of
visibility to SC members. This visibility affects the capability of SC managers in the focal
firm to identify the SC hazards and their consequences and, therefore, improve the
planning for adverse events. I also explained how some decision rules related to SC
operating system such as the inventory strategy could influence the intermediate ability of
SC to react to adverse events. By interpreting my case data in the light of the existing
academic literature, I can formulate some specific propositions.There is an increasing interest in the resilience of supply chains given the growing
awareness of their vulnerabilities to natural and man-made hazards. Contemporary
academic literature considers, for example, so-called resilience enablers and strategies,
such as improving the nature of collaboration and flexibility within the supply chain.
Efforts to analyse resilience tend to view the supply chain as a complex system. The
present research adopts a distinctive approach to the analysis of supply resilience by
building formal models from the perspective of the responsible manager. Dynamic
Bayesian Networks (DBNs) are selected as the modelling method since they are capable
of representing the temporal evolution of uncertainties affecting supply. They also
support probabilistic analysis to estimate the impact of potentially hazardous events
through time. In this way, the recovery rate of the supply chain under mitigation action
scenarios and an understanding of resilience can be obtained.
The research is grounded in multiple case studies of manufacturing and retail supply
chains, involving focal companies in the UK, Canada and Malaysia, respectively. Each
case involves building models to estimate the resilience of the supply chain given
uncertainties about, for example, business continuity, lumpy spare parts demand and
operations of critical infrastructure. DBNs have been developed by using relevant data
from historical empirical records and subjective judgement. Through the modelling
practice, It has been found that some SC characteristics (i.e. level of integration,
structure, SC operating system) play a vital role in shaping and quantifying DBNs and
reduce their elicitation burden. Similarly, It has been found that the static and dynamic
discretization methods of continuous variables affect the DBNs building process.
I also studied the effect of level of integration, visibility, structure and SC operating
system on the resilience level of SCs through the analysis of DBNs outputs. I found that
the influence of the integration intensity on supply chain resilience can be revealed
through understanding the dependency level of the focal firm on SC members resources. I
have also noticed the relationship between the span of integration and the level of
visibility to SC members. This visibility affects the capability of SC managers in the focal
firm to identify the SC hazards and their consequences and, therefore, improve the
planning for adverse events. I also explained how some decision rules related to SC
operating system such as the inventory strategy could influence the intermediate ability of
SC to react to adverse events. By interpreting my case data in the light of the existing
academic literature, I can formulate some specific propositions