The majority of individuals with Parkinson's disease (PD) experience voice and speech difficulties at some point over the course of the disease. Voice therapy has been found to help improve voice and speech in individuals with PD, but the majority of these individuals do not enroll in voice therapy. The purpose of this study was to determine whether watching short videos about voice symptoms and treatment in Parkinson's disease influences readiness to change, stages of change, and self-efficacy in individuals with PD. Eight individuals with PD participated in the study. Fifteen videos were chosen, three representing each of the five stages of change. We chose videos from YouTube that represented variety in speakers, content, and genre. We found that readiness to change significantly increased after watching videos, suggesting that watching videos helped these individuals move closer to actively improving their voice and speech. In addition, five of the eight participants showed forward movement in stages of change. Finally, self-efficacy demonstrated a positive trend following video watching. Overall, our results demonstrate that watching videos available on the internet can influence individuals with Parkinson's disease in changing vocal behavior. Implications for future wireless health applications are described.
Activity recognition using wearable motion sensors plays an important role in pervasive wellness and healthcare monitoring applications. The activity recognition algorithms are often designed to work with a known orientation of sensors on the body. In the case of accidental displacement of the motion sensors, it is important to identify the new sensor location and orientation. This step, often called calibration or recalibration, requires extra effort from the user to either perform a set of known movements, or enter information about the placement of the sensors manually. In this paper, we propose a camera-assisted calibration approach that does not require any extra effort from the user. The calibration is done seamlessly when the user appears in front of the camera (in our case, a Kinect camera) and performs an arbitrary activity of choice (e.g., walking in front of the camera). We provide experimental results supporting the effectiveness of our approach.
Gait and postural control are important aspects of human movement and balance. Normal movement control in human is subject to change with aging. With aging, the nervous system comprising, somatosensory, visual senses, spatial orientation senses, and neuromuscular control degrade. As a result, the body movement control such as the lateral sway while walking is affected which has been shown to be a significant cause of falling among the elderly. Biofeedback has been investigated to assist elderly improve their body movement and postural ability, by supplementing the feedback to the nervous system. In this paper, we propose a wearable low-power sensor system capable of characterizing lateral sway and gait parameters. Then, it can provide corrective feedback to reduce excessive sway in real-time via vibratory feedback modules. Real-time and low-power characteristics along with wearability of our proposed system allow long-term continuous subjects' sway monitoring while giving direct feedback to enhance walking sway and prevent falling. It can also be used in the clinics as a tool for evaluating the risks of falls, and training users to better maintain their balance. The effectiveness of the biofeedback system was evaluated on 12 older adults as they performed gait and stance tasks with and without biofeedback. Significant improvement (p-value < 0.1) in sway angle in variance of the sway angle, variance of gait phases, and in postural control while on perturbed surface was detected when the proposed Sway Error Feedback System was used.
Daily living activity monitoring is important for early detection of the onset of many diseases and for improving quality of life especially in elderly. A wireless wearable network of inertial sensor nodes can be used to observe daily motions. Continuous stream of data generated by these sensor networks can be used to recognize the movements of interest. Dynamic Time Warping (DTW) is a widely used signal processing method for time-series pattern matching because of its robustness to variations in time and speed as opposed to other template matching methods. Despite this flexibility, for the application of activity recognition, DTW can only find the similarity between the template of a movement and the incoming samples, when the location and orientation of the sensor remains unchanged. Due to this restriction, small sensor misplacements can lead to a decrease in the classification accuracy. In this work, we adopt DTW distance as a feature for real-time detection of human daily activities like sit to stand in the presence of sensor misplacement. To measure this performance of DTW, we need to create a large number of sensor configurations while the sensors are rotated or misplaced. Creating a large number of closely spaced sensors is impractical. To address this problem, we use the marker based optical motion capture system and generate simulated inertial sensor data for different locations and orientations on the body. We study the performance of the DTW under these conditions to determine the worst-case sensor location variations that the algorithm can accommodate.
Hidden Markov Model (HMM) is a well established technique for detecting patterns in a stream of observations. It performs well when the observation sequence does not contain unseen patterns that were not part of the training set. In an unconstrained environment, the observation sequence might contain new patterns that the HMM model is not familiar with. In such cases, HMM will match the test pattern to some trained pattern, which is most similar to the test pattern. This increases the false positives in the system. In this paper, we are describing a threshold based technique to detect such irrelevant patterns in a continuous stream of observations, and classify them as unwanted or bad patterns. The novelty of our approach is that it allows early detection of unwanted patterns. Test patterns are validated on a fixed length substring of observation sequence, rather than on the whole observation sequence corresponding to the test pattern. The substrings are validated based on its similarity with a valid pattern using a threshold value. This reduces the latency of detection of unwanted movement, and makes the detection process independent of duration of the various pattern classes. We evaluated this technique in the context of body sensor networks based human action recognition, and have achieved about 93 percent accuracy in detecting unwanted actions, while maintaining a 94 percent accuracy of recognizing valid actions.
