Using dynamic point light display stimuli to assess gesture deficits in schizophrenia

Using virtual reality to assess gesture performance deficits in schizophrenia patients

Introduction

Gesture performance deficits are prevalent in schizophrenia patients and are strongly associated with poor social communication skills and community functioning, affecting their overall quality of life. Currently, video-recording technology is widely used in clinical settings to assess gesture production deficits in schizophrenia patients. Nevertheless, the subjective evaluation of video-recordings can encumber task assessment. The present study will aim to use virtual reality to examine its potential use as an alternative tool to objectively measure gesture performance accuracy in schizophrenia patients and healthy controls.

Methods

Gesture performance in the virtual reality setting will be based on the well-established Test of Upper Limb Apraxia. Participants will be immersed in a virtual environment where they will experience themselves being embodied in a collocated virtual body seen from a first-person perspective. Motion trackers will be placed on participants' hands and elbows to track upper body movements in real-time, and to record gesture movement for later analysis. Participants will see a virtual agent sitting across from them, with a virtual table in between. The agent will perform various types of gestures and the participants' task will be to imitate those gestures as accurately as possible. Measurements from the tracking devices will be stored and analyzed to address gesture performance accuracy across groups.

Discussion

This study aims to provide objective measurements of gesture performance accuracy in schizophrenia patients. If successful, the results will provide new knowledge to the gesture literature and offer the potential for novel therapeutic interventions using virtual reality technologies. Such interventions can improve gesturing and thus advance social communication skills in schizophrenia patients.

Human-Machine Interaction via Dual Modes of Voice and Gesture Enabled by Triboelectric Nanogenerator and Machine Learning

With the development of science and technology, human-machine interaction has brought great benefits to the society. Here, we design a voice and gesture signal translator (VGST), which can translate natural actions into electrical signals and realize efficient communication in human-machine interface. By spraying silk protein on the copper of the device, the VGST can achieve improved output and a wide frequency response of 20-2000 Hz with a high sensitivity of 167 mV/dB, and the resolution of frequency detection can reach 0.1 Hz. By designing its internal structure, its resonant frequency and output voltage can be adjusted. The VGST can be used as a high-fidelity platform to effectively recover recorded music and can also be combined with machine learning algorithms to realize the function of speech recognition with a high accuracy rate of 97%. It also has good antinoise performance to recognize speech correctly even in noisy environments. Meanwhile, in gesture recognition, the triboelectric translator is able to recognize simple hand gestures and to judge the distance between hand and the VGST based on the principle of electrostatic induction. This work demonstrates that triboelectric nanogenerator (TENG) technology can have great application prospects and significant advantages in human-machine interaction and high-fidelity platforms.

Brain Stimulation and Group Therapy to Improve Gesture and Social Skills in Schizophrenia-The Study Protocol of a Randomized, Sham-Controlled, Three-Arm, Double-Blind Trial

An important component of nonverbal communication is gesture performance, which is strongly impaired in 2/3 of patients with schizophrenia. Gesture deficits in schizophrenia are linked to poor social functioning and reduced quality of life. Therefore, interventions that can help alleviate these deficits in schizophrenia are crucial. Here, we describe an ongoing randomized, double-blind 3-arm, sham-controlled trial that combines two interventions to reduce gesture deficits in schizophrenia patients. The combined interventions are continuous theta burst stimulation (cTBS) and social cognitive remediation therapy (SCRT). We will randomize 72 patients with schizophrenia spectrum disorders in three different groups of 24 patients. The first group will receive real cTBS and real SCRT, the second group will receive sham cTBS and real SCRT, and finally the third group will receive sham SCRT. Here, the sham treatments are, as per definition, inactive interventions that mimic as closely as possible the real treatments (similar to placebo). In addition, 24 age- and gender-matched controls with no interventions will be added for comparison. Measures of nonverbal communication, social cognition, and multimodal brain imaging will be applied at baseline and after intervention. The main research aim of this project will be to test whether the combination of cTBS and SCRT improves gesture performance and social functioning in schizophrenia patients more than standalone cTBS, SCRT or sham psychotherapy. We hypothesize that the patient group receiving the combined interventions will be superior in improving gesture performance.

CLINICAL TRIAL REGISTRATION

[www.ClinicalTrials.gov], identifier [NCT04106427].