Low power glove for hand functioning analysis in children with cerebral palsy

In this paper, a low-cost glove has been manufactured to monitor and analyse the hand motion for the children who suffer from the cerebral palsy. Cerebral palsy (CP) is a combination of continual disorders affect the movement’s evolution due to a non-gradual disturbance in developing fetal or infant cerebrum. An Arduino Nano microcontroller with flex and force sensors are attached to soft cloth glove to form the analysis glove. The data of this study is collected from children who have cerebral palsy, non-cerebral palsy, and children who are treating by physiotherapy and then compared with each other. The results show that the analysis glove helps the physiotherapist to assess the hand functioning problem such as difficulty in hand grip and inability to fully bend the hand figures in general and thumb figure in particular. These remarks can help physiotherapists to define the required program to improve these functions and indications

인간 기계 상호작용을 위한 강건하고 정확한 손동작 추적 기술 연구

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2021.8. 이동준.Hand-based interface is promising for realizing intuitive, natural and accurate human machine interaction (HMI), as the human hand is main source of dexterity in our daily activities. For this, the thesis begins with the human perception study on the detection threshold of visuo-proprioceptive conflict (i.e., allowable tracking error) with or without cutantoues haptic feedback, and suggests tracking error specification for realistic and fluidic hand-based HMI. The thesis then proceeds to propose a novel wearable hand tracking module, which, to be compatible with the cutaneous haptic devices spewing magnetic noise, opportunistically employ heterogeneous sensors (IMU/compass module and soft sensor) reflecting the anatomical properties of human hand, which is suitable for specific application (i.e., finger-based interaction with finger-tip haptic devices). This hand tracking module however loses its tracking when interacting with, or being nearby, electrical machines or ferromagnetic materials. For this, the thesis presents its main contribution, a novel visual-inertial skeleton tracking (VIST) framework, that can provide accurate and robust hand (and finger) motion tracking even for many challenging real-world scenarios and environments, for which the state-of-the-art technologies are known to fail due to their respective fundamental limitations (e.g., severe occlusions for tracking purely with vision sensors; electromagnetic interference for tracking purely with IMUs (inertial measurement units) and compasses; and mechanical contacts for tracking purely with soft sensors). The proposed VIST framework comprises a sensor glove with multiple IMUs and passive visual markers as well as a head-mounted stereo camera; and a tightly-coupled filtering-based visual-inertial fusion algorithm to estimate the hand/finger motion and auto-calibrate hand/glove-related kinematic parameters simultaneously while taking into account the hand anatomical constraints. The VIST framework exhibits good tracking accuracy and robustness, affordable material cost, light hardware and software weights, and ruggedness/durability even to permit washing. Quantitative and qualitative experiments are also performed to validate the advantages and properties of our VIST framework, thereby, clearly demonstrating its potential for real-world applications.손 동작을 기반으로 한 인터페이스는 인간-기계 상호작용 분야에서 직관성, 몰입감, 정교함을 제공해줄 수 있어 많은 주목을 받고 있고, 이를 위해 가장 필수적인 기술 중 하나가 손 동작의 강건하고 정확한 추적 기술 이다. 이를 위해 본 학위논문에서는 먼저 사람 인지의 관점에서 손 동작 추적 오차의 인지 범위를 규명한다. 이 오차 인지 범위는 새로운 손 동작 추적 기술 개발 시 중요한 설계 기준이 될 수 있어 이를 피험자 실험을 통해 정량적으로 밝히고, 특히 손끝 촉각 장비가 있을때 이 인지 범위의 변화도 밝힌다. 이를 토대로, 촉각 피드백을 주는 것이 다양한 인간-기계 상호작용 분야에서 널리 연구되어 왔으므로, 먼저 손끝 촉각 장비와 함께 사용할 수 있는 손 동작 추적 모듈을 개발한다. 이 손끝 촉각 장비는 자기장 외란을 일으켜 착용형 기술에서 흔히 사용되는 지자기 센서를 교란하는데, 이를 적절한 사람 손의 해부학적 특성과 관성 센서/지자기 센서/소프트 센서의 적절한 활용을 통해 해결한다. 이를 확장하여 본 논문에서는, 촉각 장비 착용 시 뿐 아니라 모든 장비 착용 / 환경 / 물체와의 상호작용 시에도 사용 가능한 새로운 손 동작 추적 기술을 제안한다. 기존의 손 동작 추적 기술들은 가림 현상 (영상 기반 기술), 지자기 외란 (관성/지자기 센서 기반 기술), 물체와의 접촉 (소프트 센서 기반 기술) 등으로 인해 제한된 환경에서 밖에 사용하지 못한다. 이를 위해 많은 문제를 일으키는 지자기 센서 없이 상보적인 특성을 지니는 관성 센서와 영상 센서를 융합하고, 이때 작은 공간에 다 자유도의 움직임을 갖는 손 동작을 추적하기 위해 다수의 구분되지 않는 마커들을 사용한다. 이 마커의 구분 과정 (correspondence search)를 위해 기존의 약결합 (loosely-coupled) 기반이 아닌 강결합 (tightly-coupled 기반 센서 융합 기술을 제안하고, 이를 통해 지자기 센서 없이 정확한 손 동작이 가능할 뿐 아니라 착용형 센서들의 정확성/편의성에 문제를 일으키던 센서 부착 오차 / 사용자의 손 모양 등을 자동으로 정확히 보정한다. 이 제안된 영상-관성 센서 융합 기술 (Visual-Inertial Skeleton Tracking (VIST)) 의 뛰어난 성능과 강건성이 다양한 정량/정성 실험을 통해 검증되었고, 이는 VIST의 다양한 일상환경에서 기존 시스템이 구현하지 못하던 손 동작 추적을 가능케 함으로써, 많은 인간-기계 상호작용 분야에서의 가능성을 보여준다.1 Introduction 1 1.1. Motivation 1 1.2. Related Work 5 1.3. Contribution 12 2 Detection Threshold of Hand Tracking Error 16 2.1. Motivation 16 2.2. Experimental Environment 20 2.2.1. Hardware Setup 21 2.2.2. Virtual Environment Rendering 23 2.2.3. HMD Calibration 23 2.3. Identifying the Detection Threshold of Tracking Error 26 2.3.1. Experimental Setup 27 2.3.2. Procedure 27 2.3.3. Experimental Result 31 2.4. Enlarging the Detection Threshold of Tracking Error by Haptic Feedback 31 2.4.1. Experimental Setup 31 2.4.2. Procedure 32 2.4.3. Experimental Result 34 2.5. Discussion 34 3 Wearable Finger Tracking Module for Haptic Interaction 38 3.1. Motivation 38 3.2. Development of Finger Tracking Module 42 3.2.1. Hardware Setup 42 3.2.2. Tracking algorithm 45 3.2.3. Calibration method 48 3.3. Evaluation for VR Haptic Interaction Task 50 3.3.1. Quantitative evaluation of FTM 50 3.3.2. Implementation of Wearable Cutaneous Haptic Interface 51 3.3.3. Usability evaluation for VR peg-in-hole task 53 3.4. Discussion 57 4 Visual-Inertial Skeleton Tracking for Human Hand 59 4.1. Motivation 59 4.2. Hardware Setup and Hand Models 62 4.2.1. Human Hand Model 62 4.2.2. Wearable Sensor Glove 62 4.2.3. Stereo Camera 66 4.3. Visual Information Extraction 66 4.3.1. Marker Detection in Raw Images 68 4.3.2. Cost Function for Point Matching 68 4.3.3. Left-Right Stereo Matching 69 4.4. IMU-Aided Correspondence Search 72 4.5. Filtering-based Visual-Inertial Sensor Fusion 76 4.5.1. EKF States for Hand Tracking and Auto-Calibration 78 4.5.2. Prediction with IMU Information 79 4.5.3. Correction with Visual Information 82 4.5.4. Correction with Anatomical Constraints 84 4.6. Quantitative Evaluation for Free Hand Motion 87 4.6.1. Experimental Setup 87 4.6.2. Procedure 88 4.6.3. Experimental Result 90 4.7. Quantitative and Comparative Evaluation for Challenging Hand Motion 95 4.7.1. Experimental Setup 95 4.7.2. Procedure 96 4.7.3. Experimental Result 98 4.7.4. Performance Comparison with Existing Methods for Challenging Hand Motion 101 4.8. Qualitative Evaluation for Real-World Scenarios 105 4.8.1. Visually Complex Background 105 4.8.2. Object Interaction 106 4.8.3. Wearing Fingertip Cutaneous Haptic Devices 109 4.8.4. Outdoor Environment 111 4.9. Discussion 112 5 Conclusion 116 References 124 Abstract (in Korean) 139 Acknowledgment 141박

Unsupervised Parkinson’s Disease Assessment

Parkinson’s Disease (PD) is a progressive neurological disease that affects 6.2 million people worldwide. The most popular clinical method to measure PD tremor severity is a standardized test called the Unified Parkinson’s Disease Rating Scale (UPDRS), which is performed subjectively by a medical professional. Due to infrequent checkups and human error introduced into the process, treatment is not optimally adjusted for PD patients. According to a recent review there are two devices recommended to objectively quantify PD symptom severity. Both devices record a patient’s tremors using inertial measurement units (IMUs). One is not currently available for over the counter purchases, as they are currently undergoing clinical trials. It has also been used in studies to evaluate to UPDRS scoring in home environments using an Android application to drive the tests. The other is an accessible product used by researchers to design home monitoring systems for PD tremors at home. Unfortunately, this product includes only the sensor and requires technical expertise and resources to set up the system. In this paper, we propose a low-cost and energy-efficient hybrid system that monitors a patient’s daily actions to quantify hand and finger tremors based on relevant UPDRS tests using IMUs and surface Electromyography (sEMG). This device can operate in a home or hospital environment and reduces the cost of evaluating UPDRS scores from both patient and the clinician’s perspectives. The system consists of a wearable device that collects data and wirelessly communicates with a local server that performs data analysis. The system does not require any choreographed actions so that there is no need for the user to follow any unwieldy peripheral. In order to avoid frequent battery replacement, we employ a very low-power wireless technology and optimize the software for energy efficiency. Each collected signal is filtered for motion classification, where the system determines what analysis methods best fit with each period of signals. The corresponding UPDRS algorithms are then used to analyze the signals and give a score to the patient. We explore six different machine learning algorithms to classify a patient’s actions into appropriate UPDRS tests. To verify the platform’s usability, we conducted several tests. We measured the accuracy of our main sensors by comparing them with a medically approved industry device. The our device and the industry device show similarities in measurements with errors acceptable for the large difference in cost. We tested the lifetime of the device to be 15.16 hours minimum assuming the device is constantly on. Our filters work reliably, demonstrating a high level of similarity to the expected data. Finally, the device is run through and end-to-end sequence, where we demonstrate that the platform can collect data and produce a score estimate for the medical professionals

A Two-Axis Goniometric Sensor for Tracking Finger Motion

The study of finger kinematics has developed into an important research area. Various hand tracking systems are currently available; however, they all have limited functionality. Generally, the most commonly adopted sensors are limited to measurements with one degree of freedom, i.e., flexion/extension of fingers. More advanced measurements including finger abduction, adduction, and circumduction are much more difficult to achieve. To overcome these limitations, we propose a two-axis 3D printed optical sensor with a compact configuration for tracking finger motion. Based on Malus’ law, this sensor detects the angular changes by analyzing the attenuation of light transmitted through polarizing film. The sensor consists of two orthogonal axes each containing two pathways. The two readings from each axis are fused using a weighted average approach, enabling a measurement range up to 180 ∘ and an improvement in sensitivity. The sensor demonstrates high accuracy (±0.3 ∘ ), high repeatability, and low hysteresis error. Attaching the sensor to the index finger’s metacarpophalangeal joint, real-time movements consisting of flexion/extension, abduction/adduction and circumduction have been successfully recorded. The proposed two-axis sensor has demonstrated its capability for measuring finger movements with two degrees of freedom and can be potentially used to monitor other types of body motion

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Hand tracking and gesture recognition using lensless smart sensors

The Lensless Smart Sensor (LSS) developed by Rambus, Inc. is a low-power, low-cost visual sensing technology that captures information-rich optical data in a tiny form factor using a novel approach to optical sensing. The spiral gratings of LSS diffractive grating, coupled with sophisticated computational algorithms, allow point tracking down to millimeter-level accuracy. This work is focused on developing novel algorithms for the detection of multiple points and thereby enabling hand tracking and gesture recognition using the LSS. The algorithms are formulated based on geometrical and mathematical constraints around the placement of infrared light-emitting diodes (LEDs) on the hand. The developed techniques dynamically adapt the recognition and orientation of the hand and associated gestures. A detailed accuracy analysis for both hand tracking and gesture classification as a function of LED positions is conducted to validate the performance of the system. Our results indicate that the technology is a promising approach, as the current state-of-the-art focuses on human motion tracking that requires highly complex and expensive systems. A wearable, low-power, low-cost system could make a significant impact in this field, as it does not require complex hardware or additional sensors on the tracked segments

Low-Cost Sensors and Biological Signals

Many sensors are currently available at prices lower than USD 100 and cover a wide range of biological signals: motion, muscle activity, heart rate, etc. Such low-cost sensors have metrological features allowing them to be used in everyday life and clinical applications, where gold-standard material is both too expensive and time-consuming to be used. The selected papers present current applications of low-cost sensors in domains such as physiotherapy, rehabilitation, and affective technologies. The results cover various aspects of low-cost sensor technology from hardware design to software optimization

A Tangible Solution for Hand Motion Tracking in Clinical Applications

Objective real-time assessment of hand motion is crucial in many clinical applications including technically-assisted physical rehabilitation of the upper extremity. We propose an inertial-sensor-based hand motion tracking system and a set of dual-quaternion-based methods for estimation of finger segment orientations and fingertip positions. The proposed system addresses the specific requirements of clinical applications in two ways: (1) In contrast to glove-based approaches, the proposed solution maintains the sense of touch. (2) In contrast to previous work, the proposed methods avoid the use of complex calibration procedures, which means that they are suitable for patients with severe motor impairment of the hand. To overcome the limited significance of validation in lab environments with homogeneous magnetic fields, we validate the proposed system using functional hand motions in the presence of severe magnetic disturbances as they appear in realistic clinical settings. We show that standard sensor fusion methods that rely on magnetometer readings may perform well in perfect laboratory environments but can lead to more than 15 cm root-mean-square error for the fingertip distances in realistic environments, while our advanced method yields root-mean-square errors below 2 cm for all performed motions.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli