Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:4658-4661. doi: 10.1109/EMBC46164.2021.9630020.
Falls are a significant health concern for individuals with traumatic brain injury (TBI). For developing effective preemptive strategies to reduce falls, it is essential to get an accurate and objective assessment of fall-risk. The current investigation evaluates the feasibility of a robotic, posturography-based fall-risk assessment to objectively quantify the risk of falls in individuals with TBI. Five individuals with chronic TBI (age: 56.2 ± 4.7 years, time since injury: 13.09±11.95 years) performed the fall-risk assessment on hunova- a commercial robotic platform for assessing and training balance. The unique assessment considers multifaceted fall-driving components, including static and dynamic balance, sit-to-stand, limits of stability, responses to perturbations, gait speed, and history of previous falls and provides a composite score for risk of falls, called silver index (SI), a number between 0 (no risk) and 100 (high risk) based on a machine learning-based predictive model. The SI score for individuals with TBI was 66±32.1 (min: 32, max: 100) - categorized as medium-to-high risk of falls. The construct validity of SI outcome was performed by evaluating its relationship with clinical outcomes of functional balance and mobility (Berg Balance Scale (BBS), Timed-Up and Go (TUG), and gait speed) as well as posturography outcomes (Center of Pressure (CoP) area and velocity). The bivariate Pearson correlation coefficient, although not statistically significant, suggested the presence of linear relationships (0.52 > r > 0.84) between SI and functional and posturography outcomes, supporting the construct validity of SI. A large sample is needed to further prove the validity of the SI outcome before it is used for meaningful interpretations of the risk of falls in individuals with TBI.Clinical Relevance- Clinical assessments of risk of falls are traditionally based on questionnaires that may lack objectivity, consistency, and accuracy. The current work tests the feasibility of using a robotic platform-based assessment to objectively quantify the risk of falls in individuals with TBI.
跌倒对于创伤性脑损伤（TBI）患者来说是一个严重的健康问题。为了制定有效的预防策略来降低跌倒风险，必须对跌倒风险进行准确和客观的评估。本研究评估了基于机器人的动静态平衡仪测试的跌倒风险评估方法的可行性，旨在客观地量化 TBI 患者的跌倒风险。5 名慢性 TBI 患者（年龄：56.2±4.7 岁，受伤后时间：13.09±11.95 年）在 hunova 机器人平台上进行跌倒风险评估，该平台用于评估和训练平衡。该独特的评估考虑了多方面的跌倒驱动因素，包括静态和动态平衡、坐站、稳定性极限、对干扰的反应、步态速度以及既往跌倒史，并根据基于机器学习的预测模型提供了一个跌倒风险综合评分，称为银指数（SI），范围在 0（无风险）到 100（高风险）之间。TBI 患者的 SI 评分为 66±32.1（最小值：32，最大值：100）-归类为中高跌倒风险。通过评估 SI 结果与功能性平衡和移动性（伯格平衡量表（BBS）、计时起立行走测试（TUG）和步态速度）以及动静态平衡仪结果（压力中心（CoP）面积和速度）之间的关系，来验证 SI 结果的结构效度。虽然双变量 Pearson 相关系数没有统计学意义，但提示 SI 与功能性和动静态平衡仪结果之间存在线性关系（0.52>r>0.84），支持 SI 的结构效度。需要更大的样本量来进一步证明 SI 结果的有效性，然后才能将其用于 TBI 患者跌倒风险的有意义的解释。临床相关性-传统的跌倒风险评估是基于问卷，可能缺乏客观性、一致性和准确性。本研究旨在测试使用基于机器人的评估平台客观地量化 TBI 患者跌倒风险的可行性。
