Gait speeds classifications by supervised modulation based machine-learning using Kinect camera

Early indication of some diseases such as Parkinson and Multiple Sclerosis often manifests with walking difficulties. Gait analysis provides vital information for assessing the walking patterns during the locomotion, especially when the outcomes are quantitative measures. This paper explores methods that can respond to the changes in the gait features during the swing stage using Kinect Camera, a low cost, marker-free, and portable device offered by Microsoft. Kinect has been exploited for tracking the skeletal positional data of body joints to assess and evaluate the gait performance. Linear kinematic gait features are extracted to discriminate between walking speeds by using five supervised modulation based machine-learning classifiers as follow: Decision Trees (DT), linear/nonlinear Support Vector Machines (SVMs), subspace discriminant and k-Nearest Neighbour (k-NN). The role of modulation techniques such as Frequency Modulation (FM) for increasing the efficiency of classifiers have been explored. The experimental results show that all five classifiers can successfully distinguish gait futures signal associated with walking patterns with high accuracy (average expected value of 86.19% with maximum of 92.9%). This validates the capability of the presented methodology in detecting key “indicators” of health events. Keywords: Gait Analysis, Kinematic Gait Features, Amplitude and Frequency Modulations, Baseband Signal, Passband Mapping, Machine-Learning, Classification Techniqu

MIT-Skywalker: considerations on the Design of a Body Weight Support System

Background To provide body weight support during walking and balance training, one can employ two distinct embodiments: support through a harness hanging from an overhead system or support through a saddle/seat type. This paper presents a comparison of these two approaches. Ultimately, this comparison determined our selection of the body weight support system employed in the MIT-Skywalker, a robotic device developed for the rehabilitation/habilitation of gait and balance after a neurological injury. Method Here we will summarize our results with eight healthy subjects walking on the treadmill without any support, with 30% unloading supported by a harness hanging from an overhead system, and with a saddle/seat-like support system. We compared the center of mass as well as vertical and mediolateral trunk displacements across different walking speeds and support. Results The bicycle/saddle system had the highest values for the mediolateral inclination, while the overhead harness body weight support showed the lowest values at all speeds. The differences were statistically significant. Conclusion We selected the bicycle/saddle system for the MIT-Skywalker. It allows faster don-and-doff, better centers the patient to the split treadmill, and allows all forms of training. The overhead harness body weight support might be adequate for rhythmic walking training but limits any potential for balance training

Kinect-based gait recognition using sequences of the most relevant joint relative angles

This paper introduces a new 3D skeleton-based gait recognition method for motion captured by a low-cost consumer level camera, namely the Kinect. We propose a new representation of human gait signature based on the spatio-temporal changes in relative angles among different skeletal joints with respect to a reference point. A sequence of joint relative angles (JRA) between two skeletal joints, computed over a complete gait cycle, comprises an intuitive representation of the relative motion patterns of the involved joints. JRA sequences originated from different joint pairs are then evaluated to find the most relevant JRAs for gait description. We also introduce a new dynamic time warping (DTW)-based kernel that takes the collection of the most relevant JRA sequences from the train and test samples and computes a dissimilarity measure. The use of DTW in the proposed kernel makes it robust in respect to variable walking speed and thus eliminates the need of resampling to obtain equal-length feature vectors. The performance of the proposed method was evaluated using a Kinect skeletal gait database. Experimental results show that the proposed method can more effectively represent and recognize human gait, as compared against some other Kinect-based gait recognition methods

Ανάπτυξη μοντέλου διαδραστικού χορού και μουσικής με χρήση τεχνικών μηχανικής μάθησης

Ο χορός και η μουσική είναι δύο συγγενικές τέχνες, η συνεργασία των οποίων διαμορφώνει ένα ενιαίο οπτικοακουστικό καλλιτεχνικό έργο. Η χρήση της τεχνολογίας έχει διαμορφώσει νέες συνθήκες στο διάλογό τους, ανοίγοντας νέους δημιουργικούς ορίζοντες. Στην παρούσα εργασία παρουσιάζουμε τη σχεδίαση, ανάπτυξη και υλοποίηση ενός διαδραστικού μοντέλου το οποίο θα συνθέτει σε πραγματικό χρόνο αυτοματοποιημένη μουσική η οποία βρίσκεται σε δομική σχέση με την ανθρώπινη κίνηση. Ένας κατάλληλος αισθητήρας τροφοδοτεί το μοντέλο με δεδομένα της ανθρώπινης κίνησης, τα οποία αξιοποιούνται για την περιγραφή παραμέτρων της κίνησης (π.χ., σχετικές αποστάσεις, ταχύτητα, κατεύθυνση κ.λπ.). Η αναγνώριση αυτών των παραμέτρων γίνεται με χρήση τεχνικών μηχανικής μάθησης, μέσω εκπαίδευσης από ένα σύνολο δεδομένων το οποίο έχει παραχθεί από πραγματικές κινήσεις χορευτή. Έπειτα, οι παράμετροι της κίνησης αντιστοιχίζονται σε παραμέτρους του ήχου (π.χ., τονικό ύψος, διάρκεια, επιλογή ήχων, επιλογή εφέ κ.λπ.). Τέλος, το μοντέλο αυτό αξιοποιείται για τη δημιουργία συγκεκριμένης performance. Για την υλοποίησή του έγινε χρήση κατάλληλου υλικού (αισθητήρας Kinect, pc, ηχεία) και λογισμικού (προγράμματα TouchDesigner, Wekinator, Max Msp, Max for Live / Ableton Live).Technology can retransform the dialogue between dance and music, providing new creative perspectives. In this work, we present the design and development of an interactive model which composes in real-time automated, structurally related to dance, music. A sensor provides the model with data of human motion (input), which are used to gather information regarding motion parameters (e.g., relative distance, speed, direction etc.). The model, trained with a real dataset of human motions and by machine learning techniques, becomes capable of recognizing these parameters. Then, the motion parameters are mapped to sonic parameters (e.g., pitch, duration, audio samples, filters etc.), aiming to create an interactive dance performance. For the implementation, we used appropriate hardware (camera-based Kinect sensor, pc and speakers) and software (TouchDesigner, Wekinator, Max Msp, Max for Live / Ableton Live)

Using 3D sensing and projecting technology to improve the mobility of Parkinson's disease patients

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonParkinson’s is a neurological condition in which parts of the brain responsible for movements becomes incapacitated over time due to the abnormal dopamine equilibrium. Freezing of Gait (FOG) is one of the main Parkinson’s Disease (PD) symptoms that affects patients not only physically but also psychologically as it prevents them from fulfilling simple tasks such as standing up or walking. Different auditory and visual cues have been proven to be very effective in improving the mobility of People with Parkinson’s (PwP). Nonetheless, many of the available methods require user intervention or devices to be worn, charged, etc. to activate the cues. This research suggests a system that can provide an unobtrusive facility to detect FOG and falling in PwP as well as monitoring and improving their mobility using laser-based visual cues casted by an automated laser system. It proposes a new indoor method for casting a set of two parallel laser lines as a dynamic visual cue in front of a subject’s feet based on the subject’s head direction and 3D location in a room. The proposed system controls the movement of a set of pan/tilt servo motors and laser pointers using a microcontroller based on the real-time skeletal information acquired from a Kinect v2 sensor. A Graphical User Interface (GUI) is created that enables users to control and adjust the settings based on the user preferences. The system was tested and trained by 12 healthy participants and reviewed by 15 PwP who suffer from frequent FOG episodes. The results showed the possibility of employing the system as an indoor and on-demand visual cue system for PwP that does not rely on the subject’s input or introduce any additional complexities to operate. Despite limitations regarding its outdoor use, feedback was very positive in terms of domestic usability and convenience, where 12/15 PwP showed interest in installing and using the system at their homes

Machine learning and autonomous system for human gait analysis based on walk speed

The quality of life and cost of care for elderly people varies dramatically between those living independently and those receiving acute or long-term care, which takes place at home, in residential care or in hospital. The common aim of national health service providers is to keep elderly people safe at their own homes for as long as possible to promote independent living, increase their quality of life and reduce hospital costs. Hence, the application of autonomous sensing systems to enhance everyday life of such population will be valuable and has been considered here. Recently, Microsoft Kinect v2 has been used for gait analysis systems, to perform data classification of gait pattern changes based on walking speeds. This system enables the tracking without the need of any markers. Moreover, the Kinect camera is considered a low-cost device, and is quick to install, even in an unprepared environment. However, the primary challenge of such a device is that it provides a low data rate which leads to a decrease in the quality of extracted features, compared to other Motion Capture Systems (MoCap). Furthermore, in the data classification stage, the performance of classification is greatly affected by the boundary between different classes which is called decision boundary. This raises other questions such as: how to weight the features from the class labels, and which kind of similarity metric can be used. To improve the quality of features, the Amplitude Modulation (AM) and Convolutional Encoder (CE) can play a major role in detection and in ranking the gait pattern changes based on walking speed. For this purpose, the collected data is mapped into a higher frequency spectrum using the AM domain. Consequently, the “AM-modified gait signal” is produced to improve the quality of extracted gait features, by increasing the level of the frequency sampling rate. In this research, the main novelty is the combination of Amplitude Modulation (AM) and Convolutional Encoder (CE) techniques in one system (AM/CE) in order to understand and identify the walking speed effects on gait parameters. The former is proposed to extract new gait features without the need to determine the gait cycle phases, while the latter is developed to classify gait data based on walking speeds. Therefore, the performance of the CE technique is improved efficiently in gait data classification by weighting the bit positions in xv Hamming Distance (HD) length, which leads to an increase in the accuracy of measurement of the similarity metric