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.

Fall prediction using behavioural modelling from sensor data in smart homes

“…However while additional features characteristic of fall risk were expected to become visible during the study, gait velocity remained the primary fall risk indicator between short and long term fall prediction. Cuaya et al (2013) made use of multiple gait aspects rather than being limited solely to gait velocity. Gait parameters were selected by a domain expert in one method and by a feature selection algorithm in another.…”

“…Gait velocity remains a strong indicator and tends to be the easiest to measure with the least specialist equipment. Cuaya et al (2013) compared two dynamic Bayesian network structures, one constructed from gait features determined by experts in the domain, while the other used parameters selected using a feature selection algorithm. In cases where predictions were made on the indication of an imminent fall, both networks performed with equal accuracy.…”

Fuzzy logic‐based risk of fall estimation using smartwatch data as a means to form an assistive feedback mechanism in everyday living activities

“…Moreover, additional nonlinear features that can be extracted from the recurrence plot analysis [12] were estimated, i.e. recurrent plot (RP) mean line length (18); RP maximum line length (19); RP recurrence rate (20); RP determinism (21); and RP Shannon entropy (22).…”

Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network

“…DBNs, as well as Bayesian networks (BNs), are increasingly being used in clinical screening and treatment decision making. For example, DBNs and BNs have been used in the domain of nosocomial infections [21], pneumonia [22], cardiac surgery [23], gait analysis [24], osteoporosis [25], oral cancer [26], colon cancer [27], cervical cancer [28], and breast cancer [29, 30, 31, 32]. Notably, [33] proposed a Bayesian network for lung cancer built from both physical and biological data (biomarkers) for the prediction of local failure in non-small cell lung cancer (NSCLC) after radiotherapy.…”