IBM Research - Healthcare and Life Sciences - 1101 Kitchawan Rd, Yorktown Heights, NY, 10598, USA.
Digital Medicine and the Pfizer Innovation Research Lab, Pfizer, 610 Main Street, Cambridge, MA, 02139, USA.
Sci Rep. 2020 Apr 30;10(1):7377. doi: 10.1038/s41598-020-64181-3.
Unconstrained human movement can be broken down into a series of stereotyped motifs or 'syllables' in an unsupervised fashion. Sequences of these syllables can be represented by symbols and characterized by a statistical grammar which varies with external situational context and internal neurological state. By first constructing a Markov chain from the transitions between these syllables then calculating the stationary distribution of this chain, we estimate the overall severity of Parkinson's symptoms by capturing the increasingly disorganized transitions between syllables as motor impairment increases. Comparing stationary distributions of movement syllables has several advantages over traditional neurologist administered in-clinic assessments. This technique can be used on unconstrained at-home behavior as well as scripted in-clinic exercises, it avoids differences across human evaluators, and can be used continuously without requiring scripted tasks be performed. We demonstrate the effectiveness of this technique using movement data captured with commercially available wrist worn sensors in 35 participants with Parkinson's disease in-clinic and 25 participants monitored at home.
不受约束的人类运动可以被分解为一系列非监督的刻板模式或“音节”。这些音节的序列可以用符号表示，并由一个统计语法来描述，该语法随外部情境上下文和内部神经状态而变化。通过首先从这些音节之间的转换构建一个马尔可夫链，然后计算这个链的平稳分布，我们通过捕捉音节之间越来越混乱的转换来估计帕金森病症状的整体严重程度，因为运动障碍的增加。与传统的由神经科医生进行的门诊评估相比，比较运动音节的平稳分布有几个优势。这种技术可以用于非约束性的家庭行为以及门诊脚本练习，它可以避免不同的人类评估者之间的差异，并且可以连续使用而无需执行脚本任务。我们使用商业上可用的腕戴传感器在 35 名门诊帕金森病患者和 25 名在家监测的参与者中捕获的运动数据来证明该技术的有效性。
