Gestural interaction and reading skills: a case study of people with Down syndrome

People with Down syndrome present cognitive difficulties that affect their reading skills. In this study, we present results about using gestural interaction with the Kinect sensor to improve the reading skills of students with Down syndrome. We found improvements in the visual association, visual comprehension, sequential memory, and visual integration after this stimulation in the experimental group compared to the control group. We also found that the number of errors and delay time in the interaction decreased between sessions in the experimental group.

Assessing the Feasibility of Using Kinect 3D Images to Predict Light Lamb Carcasses Composition from Leg Volume

This study aimed to evaluate the accuracy of the leg volume obtained by the Microsoft Kinect sensor to predict the composition of light lamb carcasses. The trial was performed on carcasses of twenty-two male lambs (17.6 ± 1.8 kg, body weight). The carcasses were split into eight cuts, divided into three groups according to their commercial value: high-value, medium value, and low-value group. Linear, area, and volume of leg measurements were obtained to predict carcass and cuts composition. The leg volume was acquired by two different methodologies: 3D image reconstruction using a Microsoft Kinect sensor and Archimedes principle. The correlation between these two leg measurements was significant (r = 0.815, p < 0.01). The models to predict cuts and carcass traits that include leg Kinect 3D sensor volume are very good in predicting the weight of the medium value and leg cuts (R2 of 0.763 and 0.829, respectively). Furthermore, the model, which includes the Kinect leg volume, explained 85% of its variation for the carcass muscle. The results of this study confirm the good ability to estimate cuts and carcass traits of light lamb carcasses with leg volume obtained with the Kinect 3D sensor.

Multi-sensor recognition of human pressure

At present, skin pressure ulcers are a common problem in the care of bedridden patients. Solving this problem usually involves turning the patient over regularly, which requires a lot of manpower and material resources. This paper designs a human body pressure recognition model, which can solve the problem of human pressure ulcers very well in combination with intelligent nursing beds. This paper collects the bone data of the human body by using the Kinect sensor, and then processes the collected data. The film pressure sensor is used to collect the pressure information of the human body, and the pressure information is matched with the bone data of the human body, so as to obtain the pressure of the corresponding part of the human body, and judge the current posture of the human body through the pressure information. When the pressure of the compression part of the human body reaches the threshold, the intelligent nursing bed automatically turns over to reduce the pressure of the compression part of the human body.

Upper Limb Movement Measurement Systems for Cerebral Palsy: A Systematic Literature Review

Quantifying the quality of upper limb movements is fundamental to the therapeutic process of patients with cerebral palsy (CP). Several clinical methods are currently available to assess the upper limb range of motion (ROM) in children with CP. This paper focuses on identifying and describing available techniques for the quantitative assessment of the upper limb active range of motion (AROM) and kinematics in children with CP. Following the screening and exclusion of articles that did not meet the selection criteria, we analyzed 14 studies involving objective upper extremity assessments of the AROM and kinematics using optoelectronic devices, wearable sensors, and low-cost Kinect sensors in children with CP aged 4–18 years. An increase in the motor function of the upper extremity and an improvement in most of the daily tasks reviewed were reported. In the population of this study, the potential of wearable sensors and the Kinect sensor natural user interface as complementary devices for the quantitative evaluation of the upper extremity was evident. The Kinect sensor is a clinical assessment tool with a unique markerless motion capture system. Few authors had described the kinematic models and algorithms used to estimate their kinematic analysis in detail. However, the kinematic models in these studies varied from 4 to 10 segments. In addition, few authors had followed the joint assessment recommendations proposed by the International Society of Biomechanics (ISB). This review showed that three-dimensional analysis systems were used primarily for monitoring and evaluating spatiotemporal variables and kinematic parameters of upper limb movements. The results indicated that optoelectronic devices were the most commonly used systems. The joint assessment recommendations proposed by the ISB should be used because they are approved standards for human kinematic assessments. This review was registered in the PROSPERO database (CRD42021257211).

A Review on the Use of Microsoft Kinect for Gait Abnormality and Postural Disorder Assessment

Gait and posture studies have gained much prominence among researchers and have attracted the interest of clinicians. The ability to detect gait abnormality and posture disorder plays a crucial role in the diagnosis and treatment of some diseases. Microsoft Kinect is presented as a noninvasive sensor essential for medical diagnostic and therapeutic purposes. There are currently no relevant studies that attempt to summarise the existing literature on gait and posture abnormalities using Kinect technology. The purpose of this study is to critically evaluate the existing research on gait and posture abnormalities using the Kinect sensor as the main diagnostic tool. Our studies search identified 458 for gait abnormality, 283 for posture disorder of which 26 studies were included for gait abnormality, and 13 for posture. The results indicate that Kinect sensor is a useful tool for the assessment of kinematic features. In conclusion, Microsoft Kinect sensor is presented as a useful tool for gait abnormality, postural disorder analysis, and physiotherapy. It can also help track the progress of patients who are undergoing rehabilitation.

Human Activity Recognition Using The Human Skeleton Provided by Kinect

In this paper, a new method is proposed for people tracking using the human skeleton provided by the Kinect sensor, Our method is based on skeleton data, which includes the coordinate value of each joint in the human body. For data classification, the Support Vector Machine (SVM) and Random Forest techniques are used. To achieve this goal, 14 classes of movements are defined, using the Kinect Sensor to extract data containing 46 features and then using them to train the classification models. The system was tested on 12 subjects, each of whom performed 14 movements in each experiment. Experiment results show that the best average accuracy is 90.2 % for the SVM model and 99 % for the Random forest model. From the experiments, we concluded that the best distance between the Kinect sensor and the human body is one meter.

Research on Gesture Recognition and Denoising Technology of Electronic Information Teaching Demonstration Based on Kinect V2

Motion sensor is a kind of sensor which is commonly used in the field of human-computer interaction. This article uses Microsoft’s Kinect sensor to get in-depth information about gesture recognition in electronic instructional demonstrations. Here, we present the Kinect V2 sensor for use in demonstrating gesture capture and recognition in electronic information instruction. In this study, depth information is used to dynamically capture electronic information teaching demonstrative gestures. In addition, a combined denoising method is also proposed, which can effectively remove the interference and noise compared with the single denoising method. Then, the researchers programmed and moved the dynamic recognition of demonstrative gestures in electronic information instruction. Compared with some traditional denoising methods, the combined denoising method can effectively remove the blur and boundary burr. This study can be applied to the field of human-computer interaction in electronic information teaching to further improve the accuracy of information.

Placement Recommendations for Single Kinect-Based Motion Capture System in Unilateral Dynamic Motion Analysis

Low-cost, portable, and easy-to-use Kinect-based systems achieved great popularity in out-of-the-lab motion analysis. The placement of a Kinect sensor significantly influences the accuracy in measuring kinematic parameters for dynamics tasks. We conducted an experiment to investigate the impact of sensor placement on the accuracy of upper limb kinematics during a typical upper limb functional task, the drinking task. Using a 3D motion capture system as the golden standard, we tested twenty-one Kinect positions with three different distances and seven orientations. Upper limb joint angles, including shoulder flexion/extension, shoulder adduction/abduction, shoulder internal/external rotation, and elbow flexion/extension angles, are calculated via our developed Kinect kinematic model and the UWA kinematic model for both the Kinect-based system and the 3D motion capture system. We extracted the angles at the point of the target achieved (PTA). The mean-absolute-error (MEA) with the standard represents the Kinect-based system’s performance. We conducted a two-way repeated measure ANOVA to explore the impacts of distance and orientation on the MEAs for all upper limb angles. There is a significant main effect for orientation. The main effects for distance and the interaction effects do not reach statistical significance. The post hoc test using LSD test for orientation shows that the effect of orientation is joint-dependent and plane-dependent. For a complex task (e.g., drinking), which involves body occlusions, placing a Kinect sensor right in front of a subject is not a good choice. We suggest that place a Kinect sensor at the contralateral side of a subject with the orientation around 30∘ to 45∘ for upper limb functional tasks. For all kinds of dynamic tasks, we put forward the following recommendations for the placement of a Kinect sensor. First, set an optimal sensor position for capture, making sure that all investigated joints are visible during the whole task. Second, sensor placement should avoid body occlusion at the maximum extension. Third, if an optimal location cannot be achieved in an out-of-the-lab environment, researchers could put the Kinect sensor at an optimal orientation by trading off the factor of distance. Last, for those need to assess functions of both limbs, the users can relocate the sensor and re-evaluate the functions of the other side once they finish evaluating functions of one side of a subject.

Development of a Tele-Rehabilitation System Using an Upper Limb Assistive Device

A tele-rehabilitation system that can achieve remote interaction between a physical therapist (PT) and a patient was developed. Patients need to execute rehabilitation exercises to maintain upper limb function. However, it is difficult for them to travel to hospitals without aid. This system is equipped with a PC and a Kinect sensor at the hospital side (i.e., the PT), and a PC and an upper limb assistive device in the patient’s home. The PT displays the motion in front of a Kinect sensor, which identifies the motion. In addition, the device on the home side assists the motion of the patient using the Internet. When the device receives a force higher than the safety value from the patient at any particular point on it, vibrators at the corresponding point on the PT’s arm start to vibrate. Thereby, the PT can identify the patient’s condition and limitations. The time delays in the transmission of data of device motion and the vibrators were measured and compared. As a result, the PT could identify the patient’s condition faster than the motion of the device.