Sinziana Mazilu

GaitAssist: a daily-life support and training system for parkinson's disease patients with freezing of gait

Patients with Parkinsons disease often experience freezing of gait, which bears a high risk of falling, a prevalent cause for morbidity and mortality. In this work we present GaitAssist, a wearable system for freezing of gait support in daily life. The system provides real-time auditory cueing after the onset of freezing episodes. Furthermore, GaitAssist implements training exercises to learn how to handle freezing situations. GaitAssist is the result of a design process where we considered the input of engineers, clinicians and 18 Parkinsons disease patients, in order to find an optimal trade-off between system wearability and performance. We tested the final system in a user study with 5 additional patients. They reported a reduction in the freezing of gait duration as a result of the auditory stimulation provided, and that they feel the system enhanced their confidence during walking.

ActionSLAM: Using location-related actions as landmarks in pedestrian SLAM

Indoor localization at minimal deployment effort and with low costs is relevant for many ambient intelligence and mobile computing applications. This paper presents ActionSLAM, a novel approach to Simultaneous Localization And Mapping (SLAM) for pedestrian indoor tracking that makes use of body-mounted sensors. ActionSLAM iteratively builds a map of the environment and localizes the user within this map. A foot-mounted Inertial Measurement Unit (IMU) keeps track of the users path, while observations of location-related actions (e.g. door-opening or sitting on a chair) are used to compensate for drift error accumulation in a particle filter framework. Location-related actions are recognizable from body-mounted IMUs that are often used in ambient-assisted living scenarios for context awareness. Thus localization relies only on on-body sensing and requires no ambient infrastructure such as Wi-Fi access points or radio beacons. We characterize ActionSLAM on a dataset of 1.69km walking in three rooms and involving 241 location-related actions. For the experimental dataset, the algorithm robustly tracked the subject with mean error of 1.2m. The simultaneously built map reflects the building layout and positions landmarks with a mean error of 0.5m. These results were achieved with a simulated action recognition system consisting of an IMU attached to the wrist of a user and a smartphone in his pocket. We found that employing more complex action recognition is not beneficial for ActionSLAM performance. Our findings are supported by evaluations in synthetic environments through simulation of IMU signals for walks in typical home scenarios.

ActionSLAM on a smartphone: At-home tracking with a fully wearable system

We present SmartActionSLAM, an Android smartphone application that performs location tracking in home and office environments. It uses the integrated motion sensors of the smartphone and an optional foot-mounted inertial measurement unit to track a person. The application implements an instance of the ActionSLAM algorithmic framework. ActionSLAM combines pedestrian dead reckoning with the observation of activities (in SmartActionSLAM: sitting and standing still) to build and update a local landmark map of the users environment. This map is used to compensate for error accumulation of dead reckoning in a particle filter framework. We show that it is possible to execute the ActionSLAM algorithm in real-time on a smartphone without platform-specific optimizations. Furthermore, we analyze the localization performance of the application in six constrained and two real-life recordings. When using only the smartphones internal sensors, tracking was adequate in most constrained setups, but failed in the real-world scenarios because of errors in recognizing irregular leg movements. By including the foot-mounted sensor, mapping with a mean landmark positioning error of <; 0.5m and robustness > 90% was achieved in all environments. Smart-ActionSLAM is fully wearable and requires no infrastructure in the environment. The approach is therefore ideally suited for rapid deployment in home and office environments, as for example required in patient monitoring studies.

Feature learning for detection and prediction of freezing of gait in parkinson's disease

Freezing of gait (FoG) is a common gait impairment among patients with advanced Parkinsons disease. FoG is associated with falls and negatively impact the patients quality of life. Wearable systems that detect FoG have been developed to help patients resume walking by means of auditory cueing. However, current methods for automated detection are not yet ideal. In this paper, we first compare feature learning approaches based on time-domain and statistical features to unsupervised ones based on principal components analysis. The latter systematically outperforms the former and also the standard in the field - Freezing Index by up to 8.1% in terms of F1-measure for FoG detection. We go a step further by analyzing FoG prediction, i.e., identification of patterns (pre-FoG) occurring before FoG episodes, based only on motion data. Until now this was only attempted using electroencephalography. With respect to the three-class problem (FoG vs. pre-FoG vs. normal locomotion), we show that FoG prediction performance is highly patient-dependent, reaching an F1-measure of 56% in the pre-FoG class for patients who exhibit enough gait degradation before FoG.

GaitAssist: A wearable assistant for gait training and rehabilitation in Parkinson's disease

Many patients with Parkinsons disease suffer from short periods during which they cannot continue walking, the so-called freezing of gait. Patients can learn to use rhythmic auditory sounds as support during these episodes. We developed GaitAssist, a personalized wearable system for freezing of gait support, that enables training in unsupervised environments. GaitAssist detects freezing episodes from ankle-mounted motion sensors, which stream data via Bluetooth to an Android phone. In response, the system plays a rhythmic auditory sound that adapts to the patients regular gait speed. While GaitAssist can be used as a daily-life assistant, it also provides support for three types of training and rehabilitation exercises. The user can create personalized training sessions by adjusting the exercise and feedback parameters.

Prediction of Freezing of Gait in Parkinson's From Physiological Wearables: An Exploratory Study

Freezing of gait (FoG) is a common gait impairment among patients with advanced Parkinsons disease. FoG is associated with falls and negatively impacts the patients quality of life. Wearable systems that detect FoG in real time have been developed to help patients resume walking by means of rhythmic cueing. Current methods focus on detection, which require FoG events to happen first, while their prediction opens the road to preemptive cueing, which might help subjects to avoid freeze altogether. We analyzed electrocardiography (ECG) and skin-conductance (SC) data from 11 subjects who experience FoG in daily life, and found statistically significant changes in ECG and SC data just before the FoG episodes, compared to normal walking. Based on these findings, we developed an anomaly-based algorithm for predicting gait freeze from relevant SC features. We were able to predict 71.3% from 184 FoG with an average of 4.2 s before a freeze episode happened. Our findings enable the possibility of wearable systems, which predict with few seconds before an upcoming FoG from SC, and start external cues to help the user avoid the gait freeze.

A Wearable Assistant for Gait Training for Parkinson’s Disease with Freezing of Gait in Out-of-the-Lab Environments

People with Parkinson’s disease (PD) suffer from declining mobility capabilities, which cause a prevalent risk of falling. Commonly, short periods of motor blocks occur during walking, known as freezing of gait (FoG). To slow the progressive decline of motor abilities, people with PD usually undertake stationary motor-training exercises in the clinics or supervised by physiotherapists. We present a wearable system for the support of people with PD and FoG. The system is designed for independent use. It enables motor training and gait assistance at home and other unsupervised environments. The system consists of three components. First, FoG episodes are detected in real time using wearable inertial sensors and a smartphone as the processing unit. Second, a feedback mechanism triggers a rhythmic auditory signal to the user to alleviate freeze episodes in an assistive mode. Third, the smartphone-based application features support for training exercises. Moreover, the system allows unobtrusive and long-term monitoring of the user’s clinical condition by transmitting sensing data and statistics to a telemedicine service. We investigate the at-home acceptance of the wearable system in a study with nine PD subjects. Participants deployed and used the system on their own, without any clinical support, at their homes during three protocol sessions in 1 week. Users’ feedback suggests an overall positive attitude toward adopting and using the system in their daily life, indicating that the system supports them in improving their gait. Further, in a data-driven analysis with sensing data from five participants, we study whether there is an observable effect on the gait during use of the system. In three out of five subjects, we observed a decrease in FoG duration distributions over the protocol days during gait-training exercises. Moreover, sensing data-driven analysis shows a decrease in FoG duration and FoG number in four out of five participants when they use the system as a gait-assistive tool during normal daily life activities at home.

Engineers meet clinicians: augmenting Parkinson's disease patients to gather information for gait rehabilitation

Many people with Parkinsons disease suffer from freezing of gait, a debilitating temporary inability to pursue walking. Rehabilitation with wearable technology is promising. State of the art approaches face difficulties in providing the needed bio-feedback with a sufficient low-latency and high accuracy, as they rely solely on the crude analysis of movement patterns allowed by commercial motion sensors. Yet the medical literature hints at more sophisticated approaches. In this work we present our first step to address this with a rich multimodal approach combining physical and physiological sensors. We present the experimental recordings including 35 motion and 3 physiological sensors we conducted on 18 patients, collecting 23 hours of data. We provide best practices to ensure a robust data collection that considers real requirements for real world patients. To this end we show evidence from a user questionnaire that the system is low-invasive and that a multimodal view can leverage cross modal correlations for detection or even prediction of gait freeze episodes.

The role of wrist-mounted inertial sensors in detecting gait freeze episodes in Parkinson’s disease

Abstract Freezing of gait (FoG) is a motor impairment among patients with advanced Parkinson’s disease, associated with falls and negative impact on patient’s quality of life. Detecting such freezes allows real-time gait monitoring to reduce the risk of falls. We investigate the correlation between wrist movements and the freezing of the gait in Parkinson’s disease, targeting FoG-detection from wrist-worn sensing data. While most of research focuses on placing inertial sensors on lower limb, i.e., foot, ankle, thigh, we focus on the wrist as an alternative placement. Commonly worn accessories at the wrist such as watches or wristbands are more likely to be accepted and easier to be worn by elderly users, especially subjects with motor problems. Experiments on data from 11 subjects with Parkinson’s disease and FoG show there are specific features from wrist movements which are related to gait freeze, such the power on different frequency ranges and statistical information from acceleration and rotation data. Moreover, FoG can be detected by using wrist motion and machine learning models with a FoG hit rate of 0.9, and a specificity between 0.66 and 0.8. Compared with the state-of-the-art lower limb information used to detect FoG, the wrist increases the number of false detected events, while preserving the FoG hit-rate and detection latency. This suggests that wrist sensors can be a feasible alternative to the cumbersome placement on the legs.

Gait, wrist, and sensors: Detecting freezing of gait in Parkinson's disease from wrist movement

We investigate the correlation between wrist movement and freezing of the gait in Parkinsons disease. Detecting such freezes allows real-time monitoring to reduce the risk of falls in subjects with Parkinsons. While most of research focuses on placing inertial sensors on lower limb, i.e., foot, ankle, thigh, lower back, we focus on the wrist as an alternative placement. Commonly worn accessories at the wrist such as watches or wristbands are easier to be accepted and worn by elderly users, in special subjects with motor problems. Experiments on data from 11 subjects show that freezing of gait episodes can be detected using the wrist movements, with a freeze hit-rate of 90% and 83% specificity in a subject-dependent evaluation scheme. This suggests that wrist sensors can be a feasible alternative to the cumbersome placement on the legs.