Gait phase detection by using a portable system and artificial neural network

Cognitive driven gait freezing phase detection and classification for neuro-rehabilitated patients using machine learning algorithms

BACKGROUND

The significance of diagnosing illnesses associated with brain cognitive and gait freezing phase patterns has led to a recent surge in interest in the study of gait for mental disorders. A more precise and effective way to characterize and classify many common gait problems, such as foot and brain pulse disorders, can improve prognosis evaluation and treatment options for Parkinson patients. Nonetheless, the primary clinical technique for assessing gait abnormalities at the moment is visual inspection, which depends on the subjectivity of the observer and can be inaccurate.

RESEARCH QUESTION

This study investigates whether it is possible to differentiate between gait brain disorder and the typical walking pattern using machine learning driven supervised learning techniques and data obtained from inertial measurement unit sensors for brain, hip and leg rehabilitation.

METHOD

The proposed method makes use of the Daphnet freezing of Gait Data Set, consisted of 237 instances with 9 attributes. The method utilizes machine learning and feature reduction approaches in leg and hip gait recognition.

RESULTS

From the obtained results, it is concluded that among all classifiers RF achieved highest accuracy as 98.9 % and Perceptron achieved lowest i.e. 70.4 % accuracy. While utilizing LDA as feature reduction approach, KNN, RF and NB also achieved promising accuracy and F1-score in comparison with SVM and LR classifiers.

SIGNIFICANCE

In order to distinguish between the different gait disorders associated with brain tissues freezing/non-freezing and normal walking gait patterns, it is shown that the integration of different machine learning algorithms offers a viable and prospective solution. This research implies the need for an impartial approach to support clinical judgment.

A BTO/PVDF/PDMS Piezoelectric Tangential and Normal Force Sensor Inspired by a Wind Chime

There is a growing demand for flexible pressure sensors in environmental monitoring and human-robot interaction robotics. A flexible and susceptible sensor can discriminate multidirectional pressure, thus effectively detecting signals of small environmental changes and providing solutions for personalized medicine. This paper proposes a multidimensional force detection sensor inspired by a wind chime structure with a three-dimensional force structure to detect and analyze normal and shear forces in real time. The force-sensing structure of the sensor consists of an upper and lower membrane on a polydimethylsiloxane substrate and four surrounding cylinders. A piezoelectric hemisphere is made of BTO/PVDF/PDMS composite material. The sensor columns in the wind chime structure surround the piezoelectric layer in the middle. When pressure is applied externally, the sensor columns are connected to the piezoelectric layer with a light touch. The piezoelectric hemisphere generates a voltage signal. Due to the particular structure of the sensor, it can accurately capture multidimensional forces and identify the direction of the external force by analyzing the position of the sensor and the output voltage amplitude. The development of such sensors shows excellent potential for self-powered wearable sensors, human-computer interaction, electronic skin, and soft robotics applications.

Self-Powered Force Sensors for Multidimensional Tactile Sensing

A tactile sensor is the centerpiece in human-machine interfaces, enabling robotics or prosthetics to manipulate objects dexterously. Specifically, it is crucial to endow the sensor with the ability to detect and distinguish normal and shear forces in real time, so that slip detection and more complex control could be achieved during the interaction with objects. Here, a self-powered multidirectional force sensor (SMFS) based on triboelectric nanogenerators with a three-dimensional structure is proposed for sensing and analysis of normal and shear forces in real time. Four polydimethylsiloxane (PDMS) cylinders act as the force sensing structure of the SMFS. A flexible tip array made of carbon black/MXene/PDMS composites is used to generate triboelectric signals when the SMFS is driven by an external force. The SMFS can sense multidimensional force due to the adaptability of the PDMS cylinders and detect tiny force due to the sensitivity of the flexible tips. A small shear force as low as 50 mN could be recognized using the SMFS. The direction of the externally applied force could be recognized by analyzing the location and output voltage amplitude of the SMFS. Moreover, the tactile sensing applications, including reagent weighing and force direction perception, are also achieved by using the SMFS, which demonstrates the potential in promoting developments of self-powered wearable sensors, human-machine interactions, electronic skin, and soft robotic applications.

Frequency-dependent modulation of neural oscillations across the gait cycle

Balance and walking are fundamental to support common daily activities. Relatively accurate characterizations of normal and impaired gait features were attained at the kinematic and muscular levels. Conversely, the neural processes underlying gait dynamics still need to be elucidated. To shed light on gait‐related modulations of neural activity, we collected high‐density electroencephalography (hdEEG) signals and ankle acceleration data in young healthy participants during treadmill walking. We used the ankle acceleration data to segment each gait cycle in four phases: initial double support, right leg swing, final double support, left leg swing. Then, we processed hdEEG signals to extract neural oscillations in alpha, beta, and gamma bands, and examined event‐related desynchronization/synchronization (ERD/ERS) across gait phases. Our results showed that ERD/ERS modulations for alpha, beta, and gamma bands were strongest in the primary sensorimotor cortex (M1), but were also found in premotor cortex, thalamus and cerebellum. We observed a modulation of neural oscillations across gait phases in M1 and cerebellum, and an interaction between frequency band and gait phase in premotor cortex and thalamus. Furthermore, an ERD/ERS lateralization effect was present in M1 for the alpha and beta bands, and in the cerebellum for the beta and gamma bands. Overall, our findings demonstrate that an electrophysiological source imaging approach based on hdEEG can be used to investigate dynamic neural processes of gait control. Future work on the development of mobile hdEEG‐based brain–body imaging platforms may enable overground walking investigations, with potential applications in the study of gait disorders.