Sensors vs. experts - A performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients

Abstract

BackgroundFall events contribute significantly to mortality, morbidity and costs in our ageing population. In order to identify persons at risk and to target preventive measures, many scores and assessment tools have been developed. These often require expertise and are costly to implement. Recent research investigates the use of wearable inertial sensors to provide objective data on motion features which can be used to assess individual fall risk automatically. So far it is unknown how well this new method performs in comparison with conventional fall risk assessment tools. The aim of our research is to compare the predictive performance of our new sensor-based method with conventional and established methods, based on prospective data.MethodsIn a first study phase, 119 inpatients of a geriatric clinic took part in motion measurements using a wireless triaxial accelerometer during a Timed Up&Go (TUG) test and a 20 m walk. Furthermore, the St. Thomas Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) was performed, and the multidisciplinary geriatric care team estimated the patients' fall risk. In a second follow-up phase of the study, 46 of the participants were interviewed after one year, including a fall and activity assessment. The predictive performances of the TUG, the STRATIFY and team scores are compared. Furthermore, two automatically induced logistic regression models based on conventional clinical and assessment data (CONV) as well as sensor data (SENSOR) are matched.ResultsAmong the risk assessment scores, the geriatric team score (sensitivity 56%, specificity 80%) outperforms STRATIFY and TUG. The induced logistic regression models CONV and SENSOR achieve similar performance values (sensitivity 68%/58%, specificity 74%/78%, AUC 0.74/0.72, +LR 2.64/2.61). Both models are able to identify more persons at risk than the simple scores.ConclusionsSensor-based objective measurements of motion parameters in geriatric patients can be used to assess individual fall risk, and our prediction model's performance matches that of a model based on conventional clinical and assessment data. Sensor-based measurements using a small wearable device may contribute significant information to conventional methods and are feasible in an unsupervised setting. More prospective research is needed to assess the cost-benefit relation of our approach.

Review of fall risk assessment in geriatric populations using inertial sensors

“…Of the variables that were assessed in more than one study, only 13 were significant (p < 0.05) each time they were assessed: 1) mediolateral and anteroposterior postural sway length [43,51]; 2) mediolateral and anteroposterior postural sway velocity [43,51]; 3) ratio of mean squared modulus for postural sway [46-48]; 4) standard deviation of anteroposterior acceleration [18,74]; 5) root mean square amplitude of vertical linear acceleration [55,72]; 6) gait speed [40,44,55,64,72,74]; 7) sit-to-stand transition duration [17,45]; 8) dominant Fast Fourier Transform (FFT) peak parameters derived from lower-back linear acceleration signals [59,61,62]; 9) ratio of even to odd harmonic magnitudes derived from head, upper back, and lower-back linear acceleration signals [44,55,57,58,64,72,75]; 10) area under the first six harmonics divided by the remaining area for lower-back linear acceleration signals [57,58]; 11) ratio of the first four harmonics to the magnitude of the first six harmonics for lower-back linear acceleration signals [57,58]; 12) maximum Lyapunov exponent of angular velocity signal [73,77]; 13) discrete wavelet transform parameters from lower-back angular velocity and linear acceleration signals and sternum linear acceleration signals [45,63]. Six of these multi-study variables (1,2,5,6,9,12) were from different research groups, while seven variables (3,4,7,8,10, 11,13) were from a single research group.…”

Predicting Geriatric Falls Following an Episode of Emergency Department Care: A Systematic Review

“…25 The ED represents a unique milieu and patient phenotype, so predictive instruments from other settings often fail in the chaotic environment of EM. 32 Nonetheless, future investigators should assess the feasibility and prognostic accuracy for future falls of clinical gestalt, 65 as well as existing instruments like the ABCS injurious fall screening tool, 66,67 CAREFALL, 68 FROP-Com, 69,70 HOME FAST, 71 Hendrich II Fall Risk Model, 72,73 STRATIFY, 74 University of Pittsburgh Medical Center screening tool, 67 New York–Presbyterian Fall and Injury Risk Assessment Tool, 73,75 Johns Hopkins Fall Risk Assessment Tool, 76 Maine Medical Center Fall Risk Assessment, 73 Morse Fall Scale, 73,77 Spartanburg Fall Risk Assessment Tool, 78 and risk scores described by Bongue et al 79 and Stel et al 80 No study has previously evaluated these instruments in ED settings. Previous systematic reviews of fall risk factors and prediction instruments neglected ED-based studies and did not report meta-analyses or LRs, but favored the Hendrich II Fall Risk Model 81 or STRATIFY instruments.…”

Use of Information Technology for Falls Detection and Prevention in the Elderly

“…The waist was then concluded as the most stable position to monitor movement using the sensor board after several tests were conducted with it in various positions such as the chest, the neck, and the waist. Marschollek et al (2011) also conducted a study with 119 geriatric inpatients wearing an accelerometer on the waist and results obtained suggest that accelerometer data may be used to predict falls in an unsupervised setting and the parameters used for prediction are measurable with an unobtrusive sensor device during normal activities of daily living. Such reliable automated fall detection can increase confidence in people with fear of falling, promote active safe living for older adults, and reduce complications from falls.…”