Objective Assessment of Overexcited Hand Movements using a Lightweight Sensory Device Sunghoon Ivan Lee ∗ , Hassan Ghasemzadeh ∗§ , Bobak Jack Mortazavi ∗ , Andrew Yew † , Ruth Getachew † , Mehrdad Razaghy † , Nima Ghalehsari † , Brian H. Paak † , Jordan H. Garst † , Marie Espinal † , Jon Kimball † , Daniel C. Lu †‡ and Majid Sarrafzadeh ∗§ ∗ Computer Science Department, † Department of Neurosurgery, ‡ Department of Orthopedic Surgery, § Wireless Health Institute University of California Los Angeles (UCLA) Los Angeles, USA Email: {silee, hassan, bobakm, majid}@cs.ucla.edu {rgetachew, jkimball, andrewyew, dclu}@mednet.ucla.edu Abstract—Hyperexcitability in hand is a disorder character- ized by exaggerated muscle movement, and is a common symptom associated with neuro-degenerative diseases and spinal cord injuries. Current assessment methods for hyperexcitability rely on subjective examination, or on methods that evaluate the overall hand grip performance without particularization in the excitation. This paper introduces a system that utilizes an inexpensive body sensor device combined with a series of signal processing units that extract information specifically related to physiological phenomena generated by hyperexcitability. A clinical cohort study has been conducted on nine patients with cervical spinal cord injuries (mean age 58.2 ± 13.5). The experimental results show that the proposed signal processing mechanism accurately detects and analyzes the body signal. The medical significance of the experimental results is also investigated. This opens up a new opportunity for patients and clinical professionals to obtain accurate feedback of patient’s motor function in an economical and ubiquitous manner. I. I NTRODUCTION Patients who suffer from neuro-degenerative diseases (e.g., stroke and Parkinson’s disease) or traumatic spinal cord in- juries often carry movement deficits in upper extremities [1], [2]. Among many motor symptoms associated with these ailments, we are particularly interested in hyperexcitability in hand muscles, which is defined as a motor disorder characterized by exaggerated tendon jerk reflexes [3] due to an excessive velocity increase in muscle tone [4]. Handgrip hyperexcitability creates involuntary forces during grasping performance, which intensely restrict daily activities requir- ing sophisticated hand muscle manipulation such as eating, clothing, and bathing. Traditional assessment methodology for hyperexcitability relied on subjective observations of muscle behavior, and as a result, many attempts have been made to objectively quantify the level of hyperexcitability. Existing solutions to quantify hyperexcitability of muscle movements have concentrated on techniques such as clinical scales, Electromyogrphic (EMG), and biomechanics. However, these techniques are often highly complicated to be deployed at clinical (or in-home) settings, large in size, and extremely expensive. As a consequence, it was not economically feasible to deploy these techniques for a large patient population, and this creates a need for an accurate and affordable assessment system [5]. Sensing platforms that can be easily deployed on the body have been actively researched and are considered as alternative approaches to diagnose, to quantify, and to rehabilitate patients with motor deficits such as in [6]. Body sensing systems utilize accurate, simple, and inexpensive sensors to collect physiological data in order to quantify motor performance [7], [8]. These characteristics allow (i) easy ways to collect sensory data either pervasively or from a simple motor task, (ii) economic deployment of the system for a large patient pop- ulation, and (iii) improvement in clinical benefits for patients. Clinical benefits of body sensing systems for assessing motor abnormalities include (i) economic benefits [9], (ii) frequent and continuous measurement of motor function progress over time, (iii) quantifying the effectiveness of medical treatments, such as surgical operations or medications, and (iv) early diagnosis of motor function for potential patients. In this paper, a low-cost system that objectively quantifies the level of hyperexcitability in hand dexterity is introduced. A term activation hypertonia is used to describe the hy- perexcitability during voluntary grip contraction (details are provided in Section IV). The proposed system utilizes a lightweight handgrip sensory device to assess the level of activation hypertonia, which makes the system highly portable. The system provides a simple target tracking task to examine fine hand motor skills for patients with cervical spinal cord injuries [10]. The collected body signals are then analyzed by a series of four signal processing units: (i) the pre-processing unit, (ii) the abnormality (i.e. activation hypertonia) detection unit, (iii) the abnormality analytic unit, and (iv) the parameter extraction unit. The preprocessing unit performs a low-pass fil- ter to reduce noise in the raw signals, and segments the signals into a number of subsignals. The detection unit statistically determines whether a resultant subsignal contains the outcome of the exaggerated muscle tone using machine learning al- gorithms. If activation hypertonia is noted, the analytic unit performs an in-depth analysis to locate important geometric points using dynamic time warping (DTW). The parameter extraction unit extracts important variables that characterize the severity of activation hypertonia. The system has been clinically tried in cohort study under collaboration with the UCLA Department of Neurosurgery in order to evaluate its performance. 978-1-4799-0330-6/13/$31.00 ©2013 IEEE