Neuromechanics and Assistive Robotics Laboratory, University of Waterloo, 200 University Ave W, Waterloo, ON N2L 3G1, Canada.
Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, Canada.
Sensors (Basel). 2020 Sep 5;20(18):5046. doi: 10.3390/s20185046.
Spasticity, a common symptom in patients with upper motor neuron lesions, reduces the ability of a person to freely move their limbs by generating unwanted reflexes. Spasticity can interfere with rehabilitation programs and cause pain, muscle atrophy and musculoskeletal deformities. Despite its prevalence, it is not commonly understood. Widely used clinical scores are neither accurate nor reliable for spasticity assessment and follow up of treatments. Advancement of wearable sensors, signal processing and robotic platforms have enabled new developments and modeling approaches to better quantify spasticity. In this paper, we review quantitative modeling techniques that have been used for evaluating spasticity. These models generate objective measures to assess spasticity and use different approaches, such as purely mechanical modeling, musculoskeletal and neurological modeling, and threshold control-based modeling. We compare their advantages and limitations and discuss the recommendations for future studies. Finally, we discuss the focus on treatment and rehabilitation and the need for further investigation in those directions.
痉挛是上运动神经元损伤患者的常见症状，通过产生不必要的反射来降低患者自由移动四肢的能力。痉挛会干扰康复计划，并导致疼痛、肌肉萎缩和骨骼肌肉畸形。尽管它很常见，但人们并不了解它。广泛使用的临床评分既不准确也不可靠，无法用于评估痉挛和治疗随访。可穿戴传感器、信号处理和机器人平台的进步为更好地量化痉挛提供了新的发展和建模方法。本文综述了用于评估痉挛的定量建模技术。这些模型生成客观的措施来评估痉挛，并使用不同的方法，如纯粹的机械建模、骨骼肌肉和神经建模，以及基于阈值控制的建模。我们比较了它们的优缺点，并讨论了对未来研究的建议。最后，我们讨论了治疗和康复的重点以及在这些方向上进一步研究的必要性。
