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Do you know how to assess the Fall Risk?

A look at the Fall Risk protocol

Falls are one of the greatest causes of serious health problems among older adults, representing more than 50% of the hospitalizations due to lesions in this age group [1]. Falls have a multifactorial origin, which includes mobility problems, balance disorders, chronic illnesses, and impaired vision. Thus, it is important to incorporate fall risk screening in the elder’s health monitoring routine.

PhysioSensing Balance Software has a protocol named Fall Risk that allows the identification of potential elderly people at risk of falling. This tool helps health professionals to categorize the patient’s fall risk in low, moderate or high.

How is it done?

This protocol measures the static balance with a pressure/force plate in four conditions:

1. Comfortable stance with eyes open (looking ahead at a point at eye level);

2. Comfortable stance with eyes closed;

3. Narrow stance with eyes open (looking ahead at a point at eye level);

4. Narrow stance with eyes closed.

Figure 1 – Interface of Fall Risk protocol, for comfortable and narrow stance.

Each condition as one trial of 45 seconds. Patient should stay still during assessment and hands should be placed on the hips.

Measures calculated

After performing all the conditions of the protocol, the value of the sway velocity index (SVI) for each of the conditions appears. This index is based on the mediolateral velocity divided by the height of the patient, and then normalized by the natural logarithm function. Mediolateral velocity is the displacement of center of pressure during the trial divided by the time (mm/s). This concept can be observed in the top left image of Figure 2, in which for each trial the software will calculate the sway displacement in the mediolateral direction during each acquisition (100 acquisitions per second) and present the mean result in the end.

Figure 2– Illustration of center of gravity (CoG) and sway displacement in mediolateral and anteroposterior directions. The body sway can be translated into center of pressure values in the mediolateral and anteroposterior directions (statokinesigram), and the software posteriorly analyzes the COP displacement during the trial (stabilogram) to calculate the mean mediolateral velocity.

Then, the SVI results are compared to age-dependent normative data, as illustrated in Figure 3. The normative values are for individuals over the age of 50 years.

Figure 3 - Example of the results section graph. The normative values for each condition are presented next to the results. The color of the bars will be green, yellow or red depending on the results.

The software also provides the mediolateral velocity of the center of pressure, the ellipse area containing 95% of the COP values and the Z-Score (standard deviations of the SVI from the mean value indicated in the normative values).

In addition, the center of pressure trace for each condition as wells as the representation of the calculated ellipse can be observed (Figure 4).

Figure 4 - Pressure center trace example for narrow stance with representation of ellipse.

All this information can be quickly exported to a PDF report, and also view the progress between evaluations. This allows a more objective and quantitative look at the risk of falling through the SVI results on weekly or monthly basis.


The Fall Risk test protocol is based on research from the University of Dayton [2] and the University of Jyväskylä in Finland [3]. Results within the normative values are indicative of a low risk of falling. This protocol can be a preliminary screening tool for the risk of falling, since mediolateral sway velocity was identified as a parameter that best differentiates recurrent fallers and non-recurrent fallers in each testing condition.

Sway velocity index values higher than the normative values are suggestive of postural control deficits. In this case, the health professional should do a more in-depth risk assessment (such as falls history, fear of falling, medication, lower limb strength, proprioception and vestibular or visual deficiencies) and recommend strategies to prevent falls and reduce the chance of injury.

Nevertheless, several sway parameters extracted from center of pressure data have shown to correlate to fall risk. A recent systematic review from F. Quijoux, et al [4] pointed that sway area and radial mean velocity (combination of ML and AP velocity) can allow discrimination between fallers and non-fallers.

It is important to point out, that instrumented assessment of sway can also be a useful tool to help healthcare professionals guide and evaluate the effectiveness and efficiency of rehabilitation programs to reduce their patients’ risk of falling and prevents falls.

Fall Prevention

Sensing Future, along with Fraunhofer AICOS and Coimbra Health School created the FallSensing, a solution for fall risk screening and implementation of fall prevention exercises, while providing biofeedback during the execution of the exercises (Figure 5). The exercises are monitored with two wearable inertial sensors and the PhysioSensing pressure platform for mobility, strength and balance assessment. During three years, FallSensing screening tool allowed fall risk assessment of 537 community-dwelling adults aged 50 years. While the FallSensing intervention tools were implemented in 69 elderly people, in which several exercises from the Otago program were integrated. The project results indicated that the technology used were suitable for exercise tracking during fall prevention exercises, in which range of motion, weight distribution and shifting, balance and cycle identification were successfully monitored for all exercises [5]–[7].

Figure 5 - Example of FallSensing practice. Patient standing over PhysioSensing pressure platform with two inertial sensors in the legs, giving biofeedback for the patient during the fall prevention exercises.

Cláudia Tonelo

Sensing Future Technologies


[1] World Health Organization, Ed., WHO global report on falls prevention in older age, 2007,

[2] K. Edginton Bigelow and N. Berme, “Development of a Protocol for Improving the Clinical Utility of Posturography as a Fall-Risk Screening Tool,” The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, vol. 66A, no. 2, pp. 228–233, Feb. 2011, doi: 10.1093/gerona/glq202.

[3] S. Pajala, P. Era, M. Koskenvuo, J. Kaprio, T. Tormakangas, and T. Rantanen, “Force Platform Balance Measures as Predictors of Indoor and Outdoor Falls in Community-Dwelling Women Aged 63-76 Years,” The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, vol. 63, no. 2, pp. 171–178, Feb. 2008, doi: 10.1093/gerona/63.2.171.

[4] F. Quijoux et al., “Center of pressure displacement characteristics differentiate fall risk in older people: A systematic review with meta-analysis,” Ageing Research Reviews, vol. 62, p. 101117, Sep. 2020, doi: 10.1016/j.arr.2020.101117.

[5] J. Silva, I. Sousa, and J. S. Cardoso, “Fusion of Clinical, Self-Reported, and Multisensor Data for Predicting Falls,” IEEE J Biomed Health Inform, vol. 24, no. 1, pp. 50–56, Jan. 2020, doi: 10.1109/JBHI.2019.2951230.

[6] J. Silva et al., “Comparing Machine Learning Approaches for Fall Risk Assessment:,” in Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies, Porto, Portugal, 2017, pp. 223–230. doi: 10.5220/0006227802230230.

[7] J. Silva, D. Moreira, J. Madureira, E. Pereira, A. Dias, and I. Sousa, “A Technological Solution for Supporting Fall Prevention Exercises at the Physiotherapy Clinic,” in 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Rome, Italy, Jun. 2018, pp. 1–6. doi: 10.1109/MeMeA.2018.8438811.

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