Posts Tagged Cameras

[Abstract] A novel smartphone camera-LED Communication for clinical signal transmission in mHealth-rehabilitation system – IEEE Conference Publication

Abstract:

In this paper, an implementation of mobile-Visible Light Communication (mVLC) technology for clinical data transmission in home-based mobile-health (mHealth) rehabilitation system is introduced. Mobile remote rehabilitation program is the solutions for improving the quality of care of the clinicians to the patients with chronic condition and disabilities. Typically, the program inquires routine exercise which obligate patients to wear wearable electronic sensors for hours in a specific range of time. Thus it motivate us to develop a novel harmless biomedical communicating system since most of the device’s protocol was based on RF communication technology which risky for a human body in term of long term usage due to RF exposure and electromagnetic interference (EMI). The proposed system are designed to utilize a visible light as a medium for hazardless-communication between wearable sensors and a mobile interface device (smartphone). Multiple clinical data such as photoplethysmogram (PPG), electrocardiogram (ECG), and respiration signal are transmitted through LED and received by a smartphone camera. Furthermore, a smartphone also used for local interface and data analyzer henceforth sent the data to the cloud for further clinician’s supervision.

I. Introduction

Home-based rehabilitation are focused to improve the care quality of the clinicians to the patients. It helps the medical experts and clinicians to monitor their patients without direct interaction to the patients. For patients, it helps them to keep the intense care of their clinical states while being at home and also helps some patients with inability to leave their home to easily interact with their doctor for treatment. Basically each individual patients and diseases have different rehabilitation treatment, such as smart exercise bike for Parkinson’s disease [1], cycling exercise for chronic disease [2], seated exercises for older adults [3], and movement disorders patients [4], also hand exercise for postStroke patients [5]. Most of the mentioned rehabilitation program are required a regular time of exercise treatment, for example based on American Heart Association / American Stroke Association (AHA/ASA) guideline [6], for inpatient rehabilitation facilities (IRFs) at least 3 hours/day with 5 days/week is required. Moreover other researcher [7], mentioned the same treatment timeline requirement for their proposed home stroke rehabilitation and monitoring system.

Source: A novel smartphone camera-LED Communication for clinical signal transmission in mHealth-rehabilitation system – IEEE Conference Publication

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[Abstract] Accurate upper body rehabilitation system using kinect

Abstract:

The growing importance of Kinect as a tool for clinical assessment and rehabilitation is due to its portability, low cost and markerless system for human motion capture. However, the accuracy of Kinect in measuring three-dimensional body joint center locations often fails to meet clinical standards of accuracy when compared to marker-based motion capture systems such as Vicon. The length of the body segment connecting any two joints, measured as the distance between three-dimensional Kinect skeleton joint coordinates, has been observed to vary with time. The orientation of the line connecting adjoining Kinect skeletal coordinates has also been seen to differ from the actual orientation of the physical body segment. Hence we have proposed an optimization method that utilizes Kinect Depth and RGB information to search for the joint center location that satisfies constraints on body segment length and as well as orientation. An experimental study have been carried out on ten healthy participants performing upper body range of motion exercises. The results report 72% reduction in body segment length variance and 2° improvement in Range of Motion (ROM) angle hence enabling to more accurate measurements for upper limb exercises.

I. Introduction

Body joint movement analysis is extremely essential for health monitoring and treatment of patients with neurological disorders and stroke. Chronic hemiparesis of the upper extremity following a stroke causes major hand movement limitations. There is possibility of permanent reduction in muscle coactivation and corresponding joint torque patterns due to stroke [1]. Several studies suggest that abnormal coupling of shoulder adductors with elbow extensors and shoulder abductors with elbow flexors often leads to some stereotypical movement characteristics exhibited by severe stroke patients [2]. Therefore continuous and effective rehabilitation therapy is absolutely essential to monitor and control such abnormalities. There is a substantial need for home-based rehabilitation post-clinical therapy.

Source: Accurate upper body rehabilitation system using kinect – IEEE Xplore Document

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[Abstract] Design of a thumb module for the FINGER rehabilitation robot

Abstract:

This paper describes the design and initial prototype of a thumb curling exoskeleton for movement therapy. This add-on device for the Finger INdividuating Grasp Exercise Robot (FINGER) guides the thumb through a single-degree-of-freedom naturalistic grasping motion. This motion complements the grasping motions of the index and middle fingers provided by FINGER. The kinematic design and mechanism synthesis described herein utilized 3D motion capture and included the determination of the principle plane of the thumb motion for the simple grasping movement. The results of the design process and the creation of a first prototype indicate that this thumb module for finger allows naturalistic thumb motion that expands the capabilities of the FINGER device.

Source: IEEE Xplore Document – Design of a thumb module for the FINGER rehabilitation robot

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