Deep Neural Network-Based Video Processing to Obtain Dual-Task Upper-Extremity Motor Performance Toward Assessment of Cognitive and Motor Function

• Main Authors: Zilong Liu (Author), Changhong Wang (Author), Guanzheng Liu (Author), Bijan Najafi (Author)
• Format: Book
• Published: IEEE, 2023-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1558-0210; 10.1109/TNSRE.2022.3228073
• Keywords: Dementia; motoric cognitive risk syndrome; telehealth; tele-medicine; deep residual neural network; mobile health; Medical technology; R855-855.5; Therapeutics. Pharmacology; RM1-950
• Source: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 574-580 (2023)

Dementia is an increasing global health challenge. Motoric Cognitive Risk Syndrome (MCR) is a predementia stage that can be used to predict future occurrence of dementia. Traditionally, gait speed and subjective memory complaints are used to identify older adults with MCR. Our previous studies indicated that dual-task upper-extremity motor performance (DTUEMP) quantified by a single wrist-worn sensor was correlated with both motor and cognitive function. Therefore, the DTUEMP had a potential to be used in the diagnosis of MCR. Instead of using inertial sensors to capture kinematic data of upper-extremity movements, here we proposed a deep neural network-based video processing model to obtain DTUEMP metrics from a 20-second repetitive elbow flexion-extension test under dual-task condition. In details, we used a deep residual neural network to obtain joint coordinate set of the elbow and wrist in each frame, and then used optical flow method to correct the joint coordinates generated by the neural network. The coordinate sets of all frames in a video recording were used to generate an angle sequence which represents rotation angle of the line between the wrist and elbow. Then, the DTUEMP metrics (the mean and SD of flexion and extension phase) were derived from angle sequences. Multi-task learning (MTL) was used to assess cognitive and motor function represented by MMSE and TUG scores based on DTUEMP metrics, with single-task learning (STL) linear model as a benchmark. The results showed a good agreement (r <inline-formula> <tex-math notation="LaTeX">$\ge0.80$ </tex-math></inline-formula> and ICC <inline-formula> <tex-math notation="LaTeX">$\ge0.58$ </tex-math></inline-formula>) between the derived DTUEMP metrics from our proposed model and the ones from clinically validated sensor processing model. We also found that there were correlations with statistical significance (p &#x003C; 0.05) between some of video-derived DTUEMP metrics (i.e. the mean of flexion time and extension time) and clinical cognitive scale (Mini-Mental State Examination, MMSE). Additionally, some of video-derived DTUEMP metrics (i.e. the mean and standard deviation of flexion time and extension time) were also associated with the scores of timed-up and go (TUG) which is a gold standard to measure functional mobility. Mean absolute percentage error (MAPE) of MTL surpassed that of STL (For MMSE, MTL: 18.63&#x0025;, STL: 23.18&#x0025;. For TUG, MTL: 17.88&#x0025;, STL: 22.53&#x0025;). The experiments with different light conditions and shot angles verified the robustness of our proposed video processing model to extract DTUEMP metrics in potentially various home environments (r <inline-formula> <tex-math notation="LaTeX">$\ge0.58$ </tex-math></inline-formula> and ICC <inline-formula> <tex-math notation="LaTeX">$\ge0.71$ </tex-math></inline-formula>). This study shows possibility of replacing sensor processing model with video processing model for analyzing the DTUEMP and a promising future to adjuvant diagnosis of MCR via a mobile platform.