Effect of Aging on Human Postural Control and the Interaction with Attention

The ability to stand upright and walk is generally taken for granted, yet control of balance utilizes many processes involving the neuromuscular and sensory systems. As we age, balance function begins to decline and can become problematic for many older adults. In particular, adults 65 years of age and older exhibit a higher incidence of falls than younger adults, and falls are a leading cause of injury in older adults, contributing to significant medical costs. Without better understanding of the impact of aging on balance and means to ameliorate those effects, this problem is expected to grow as life expectancy continues to increase.In addition to sensori-motor declines with age that impact balance, another factor known to affect balance, particularly in older adults, is attention, meaning the amount of cognitive resources utilized for a particular task. When two or more tasks vie for cognitive resources, performance in one or more tasks can be compromised (a common example today is driving while talking on a cell phone). Attention has been observed to be a critical factor in many falls reported by older adults. However, it is still not fully understood how aging and attentional demand affect balance and how they interact with each other.In this dissertation, we conducted dual-task experiments and model-based analyses to study upright standing and the interaction of the effects of age and attention on postural control. The effect of age was investigated by testing two age groups (young and older adults) with no evident balance and cognitive impairment and by comparing results of the two groups. The effect of attention and its interaction with age was studied by comparing body sway in the two age groups in response to a moving platform, while either concurrently performing a cognitive task (dual-task) or not (single-task). Our findings highlight postural control differences between young and older adults, as quantified by experimental measures of body motion as well as by model parameter values, such as stiffness, damping and processing delay

Identification of human joint impedance using a wearable powered knee exoskeleton

Joint impedance is the mechanical property that describes the dynamical relationship between joint angle and torque. It provides a description of the neuromechanical behavior of a joint and it is regulated according to the surrounding environment to promote a stable interaction with it. Joint impedance has been shown to be successfully estimated using system identification techniques on humans experimentally1 . Estimation of joint impedance is critical in post-stroke individuals particularly when they are in the chronic state of the pathology (after six months from onset). After this time, the affected limbs commonly show signs of increased resistance to movements. This condition of altered joint impedance is clinically described as joint hyper-resistance2 . The presence of hyper-resistance provokes pain, restricts the range of motion of the affected joints, limits the achievement of functional tasks, and might lead to health complications, not including worsening of the quality of life.Peer ReviewedPostprint (published version

Modelling, optimization, and simulation of a robotic assistive device for walking

Human movement disorders such as stroke can result in severe long-term motor disability affecting daily life. Walking difficulty is a key aspect of motor disorders and the most important deficit patients want to overcome. Rehabilitation technology has achieved great clinical efficacy for motor impaired patients. However, limited customization, low acceptability, technical complexity, and high cost are unresolved. Thus, further investigation is necessary. In this work, we propose a model-based framework to study the interaction dynamics between the user and a gait assistance robotic device. Model parameters were estimated from motion data of a healthy subject while wearing the device.Peer ReviewedPostprint (published version

Motion Biomarkers Showing Maximum Contrast Between Healthy Subjects and Parkinson's Disease Patients Treated With Deep Brain Stimulation of the Subthalamic Nucleus. A Pilot Study

Background: Classic motion abnormalities in Parkinson's disease (PD), such as tremor, bradykinesia, or rigidity, are well-covered by standard clinical assessments such as the Unified Parkinson's Disease Rating Scale (UPDRS). However, PD includes motor abnormalities beyond the symptoms and signs as measured by UPDRS, such as the lack of anticipatory adjustments or compromised movement smoothness, which are difficult to assess clinically. Moreover, PD may entail motor abnormalities not yet known. All these abnormalities are quantifiable via motion capture and may serve as biomarkers to diagnose and monitor PD. Objective: In this pilot study, we attempted to identify motion features revealing maximum contrast between healthy subjects and PD patients with deep brain stimulation (DBS) of the nucleus subthalamicus (STN) switched off and on as the first step to develop biomarkers for detecting and monitoring PD patients' motor symptoms. Methods: We performed 3D gait analysis in 7 out of 26 PD patients with DBS switched off and on, and in 25 healthy control subjects. We computed feature values for each stride, related to 22 body segments, four time derivatives, left–right mean vs. difference, and mean vs. variance across stride time. We then ranked the feature values according to their distinguishing power between PD patients and healthy subjects. Results: The foot and lower leg segments proved better in classifying motor anomalies than any other segment. Higher degrees of time derivatives were superior to lower degrees (jerk > acceleration > velocity > displacement). The averaged movements across left and right demonstrated greater distinguishing power than left–right asymmetries. The variability of motion was superior to motion's absolute values. Conclusions: This small pilot study identified the variability of a smoothness measure, i.e., jerk of the foot, as the optimal signal to separate healthy subjects' from PD patients' gait. This biomarker is invisible to clinicians' naked eye and is therefore not included in current motor assessments such as the UPDRS. We therefore recommend that more extensive investigations be conducted to identify the most powerful biomarkers to characterize motor abnormalities in PD. Future studies may challenge the composition of traditional assessments such as the UPDRS

Correlations between Motor Symptoms across Different Motor Tasks, Quantified via Random Forest Feature Classification in Parkinson’s Disease

BackgroundObjective assessments of Parkinson’s disease (PD) patients’ motor state using motion capture techniques are still rarely used in clinical practice, even though they may improve clinical management. One major obstacle relates to the large dimensionality of motor abnormalities in PD. We aimed to extract global motor performance measures covering different everyday motor tasks, as a function of a clinical intervention, i.e., deep brain stimulation (DBS) of the subthalamic nucleus.MethodsWe followed a data-driven, machine-learning approach and propose performance measures that employ Random Forests with probability distributions. We applied this method to 14 PD patients with DBS switched-off or -on, and 26 healthy control subjects performing the Timed Up and Go Test (TUG), the Functional Reach Test (FRT), a hand coordination task, walking 10-m straight, and a 90° curve.ResultsFor each motor task, a Random Forest identified a specific set of metrics that optimally separated PD off DBS from healthy subjects. We noted the highest accuracy (94.6%) for standing up. This corresponded to a sensitivity of 91.5% to detect a PD patient off DBS, and a specificity of 97.2% representing the rate of correctly identified healthy subjects. We then calculated performance measures based on these sets of metrics and applied those results to characterize symptom severity in different motor tasks. Task-specific symptom severity measures correlated significantly with each other and with the Unified Parkinson’s Disease Rating Scale (UPDRS, part III, correlation of r2 = 0.79). Agreement rates between different measures ranged from 79.8 to 89.3%.ConclusionThe close correlation of PD patients’ various motor abnormalities quantified by different, task-specific severity measures suggests that these abnormalities are only facets of the underlying one-dimensional severity of motor deficits. The identification and characterization of this underlying motor deficit may help to optimize therapeutic interventions, e.g., to “automatically” adapt DBS settings in PD patients

The reliance on vestibular information during standing balance control decreases with severity of vestibular dysfunction

The vestibular system is involved in gaze stabilization and standing balance control. However, it is unclear whether vestibular dysfunction affects both processes to a similar extent. Therefore, the objective of this study was to determine how the reliance on vestibular information during standing balance control is related to gaze stabilization deficits in patients with vestibular dysfunction. Eleven patients with vestibular dysfunction and twelve healthy subjects were included. Gaze stabilization deficits were established by spontaneous nystagmus examination, caloric test, rotational chair test, and head impulse test. Standing balance control was assessed by measuring the body sway (BS) responses to continuous support surface rotations of 0.5° and 1.0° peak-to-peak while subjects had their eyes closed. A balance control model was fitted on the measured BS responses to estimate balance control parameters, including the vestibular weight, which represents the reliance on vestibular information. Using multivariate analysis of variance, balance parameters were compared between patients with vestibular dysfunction and healthy subjects. Robust regression was used to investigate correlations between gaze stabilization and the vestibular weight. Our results showed that the vestibular weight was smaller in patients with vestibular dysfunction than in healthy subjects (F = 7.67, p = 0.011). The vestibular weight during 0.5° peak-to-peak support surface rotations decreased with increasing spontaneous nystagmus eye velocity (ρ = -0.82, p < 0.001). In addition, the vestibular weight during 0.5° and 1.0° peak-to-peak support surface rotations decreased with increasing ocular response bias during rotational chair testing (ρ = -0.72, p = 0.02 and ρ = -0.67, p = 0.04, respectively). These findings suggest that the reliance on vestibular information during standing balance control decreases with the severity of vestibular dysfunction. We conclude that particular gaze stabilization tests may be used to predict the effect of vestibular dysfunction on standing balance control