Fusion of Unobtrusive Sensing Solutions for Sprained Ankle Rehabilitation Exercises Monitoring in Home Environments

The ability to monitor Sprained Ankle Rehabilitation Exercises (SPAREs) in home environments can help therapists ascertain if exercises have been performed as prescribed. Whilst wearable devices have been shown to provide advantages such as high accuracy and precision during monitoring activities, disadvantages such as limited battery life and users’ inability to remember to charge and wear the devices are often the challenges for their usage. In addition, video cameras, which are notable for high frame rates and granularity, are not privacy-friendly. Therefore, this paper proposes the use and fusion of privacy-friendly and Unobtrusive Sensing Solutions (USSs) for data collection and processing during SPAREs in home environments. The present work aims to monitor SPAREs such as dorsiflexion, plantarflexion, inversion, and eversion using radar and thermal sensors. The main contributions of this paper include (i) privacy-friendly monitoring of SPAREs in a home environment, (ii) fusion of SPAREs data from homogeneous and heterogeneous USSs, and (iii) analysis and comparison of results from single, homogeneous, and heterogeneous USSs. Experimental results indicated the advantages of using heterogeneous USSs and data fusion. Cluster-based analysis of data gleaned from the sensors indicated an average classification accuracy of 96.9% with Neural Network, AdaBoost, and Support Vector Machine, amongst others

Remote sensing technologies for physiotherapy assessment

The paper presents a set of remote, unobtrusive sensing technologies that can be used in upper and lower limbs rehabilitation monitoring. The advantages of using sensors based on microwave Doppler radar or infrared technologies for physiotherapy assessment are discussed. These technologies allow motion sensing at distance from monitored subject, reducing thus the discomfort produced by some wearable technologies for limbs movement assessment. The microwave radar that may be easily hidden into environment by nonmetallic parts allows remote sensing of human motion, providing information on user movements characteristics and patterns. The infrared technologies - infrared LEDs from Leap-Motion, infrared laser from Kinect depth sensor, and infrared thermography can be used for different movements' parameters evaluation. Visible for users, Leap-motion and Kinect sensors assure higher accuracy on body parts movements' detection at low computation load. These technologies are commonly used for virtual reality (VR) and augmented reality (AR) scenarios, in which the user motion patterns and the muscular activity might be analyzed. Thermography can be employed to evaluate the muscular loading. Muscular activity during movements training in physiotherapy can be estimated through skin temperature measurement before and after physical training. Issues related to the considered remote sensing technologies such as VR serious game for motor rehabilitation, signal processing and experimental results associated with microwave radar, infrared sensors and thermography for physiotherapy sensing are included in the paper.info:eu-repo/semantics/acceptedVersio

Unobtrusive hand gesture recognition using ultra-wide band radar and deep learning

Hand function after stroke injuries is not regained rapidly and requires physical rehabilitation for at least 6 months. Due to the heavy burden on the healthcare system, assisted rehabilitation is prescribed for a limited time, whereas so-called home rehabilitation is offered. It is therefore essential to develop robust solutions that facilitate monitoring while preserving the privacy of patients in a home-based setting. To meet these expectations, an unobtrusive solution based on radar sensing and deep learning is proposed. The multi-input multi-output convolutional eXtra trees (MIMO-CxT) is a new deep hybrid model used for hand gesture recognition (HGR) with impulse-radio ultra-wide band (IR-UWB) radars. It consists of a lightweight architecture based on a multi-input convolutional neural network (CNN) used in a hybrid configuration with extremely randomized trees (ETs). The model takes data from multiple sensors as input and processes them separately. The outputs of the CNN branches are concatenated before the prediction is made by the ETs. Moreover, the model uses depthwise separable convolution layers, which reduce computational cost and learning time while maintaining high performance. The model is evaluated on a publicly available dataset of gestures collected by three IR-UWB radars and achieved an average accuracy of 98.86%

Wearable kinesthetic system for capturing and classifying upper limb gesture in post-stroke rehabilitation

BACKGROUND: Monitoring body kinematics has fundamental relevance in several biological and technical disciplines. In particular the possibility to exactly know the posture may furnish a main aid in rehabilitation topics. In the present work an innovative and unobtrusive garment able to detect the posture and the movement of the upper limb has been introduced, with particular care to its application in post stroke rehabilitation field by describing the integration of the prototype in a healthcare service. METHODS: This paper deals with the design, the development and implementation of a sensing garment, from the characterization of innovative comfortable and diffuse sensors we used to the methodologies employed to gather information on the posture and movement which derive from the entire garments. Several new algorithms devoted to the signal acquisition, the treatment and posture and gesture reconstruction are introduced and tested. RESULTS: Data obtained by means of the sensing garment are analyzed and compared with the ones recorded using a traditional movement tracking system. CONCLUSION: The main results treated in this work are summarized and remarked. The system was compared with a commercial movement tracking system (a set of electrogoniometers) and it performed the same accuracy in detecting upper limb postures and movements

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

A haptic-robotic platform for upper-limb reaching stroke therapy: Preliminary design and evaluation results

<p>Abstract</p> <p>Background</p> <p>It has been shown that intense training can significantly improve post-stroke upper-limb functionality. However, opportunities for stroke survivors to practice rehabilitation exercises can be limited because of the finite availability of therapists and equipment. This paper presents a haptic-enabled exercise platform intended to assist therapists and moderate-level stroke survivors perform upper-limb reaching motion therapy. This work extends on existing knowledge by presenting: 1) an anthropometrically-inspired design that maximizes elbow and shoulder range of motions during exercise; 2) an unobtrusive upper body postural sensing system; and 3) a vibratory elbow stimulation device to encourage muscle movement.</p> <p>Methods</p> <p>A multi-disciplinary team of professionals were involved in identifying the rehabilitation needs of stroke survivors incorporating these into a prototype device. The prototype system consisted of an exercise device, postural sensors, and a elbow stimulation to encourage the reaching movement. Eight experienced physical and occupational therapists participated in a pilot study exploring the usability of the prototype. Each therapist attended two sessions of one hour each to test and evaluate the proposed system. Feedback about the device was obtained through an administered questionnaire and combined with quantitative data.</p> <p>Results</p> <p>Seven of the nine questions regarding the haptic exercise device scored higher than 3.0 (somewhat good) out of 4.0 (good). The postural sensors detected 93 of 96 (97%) therapist-simulated abnormal postures and correctly ignored 90 of 96 (94%) of normal postures. The elbow stimulation device had a score lower than 2.5 (neutral) for all aspects that were surveyed, however the therapists felt the rehabilitation system was sufficient for use without the elbow stimulation device.</p> <p>Conclusion</p> <p>All eight therapists felt the exercise platform could be a good tool to use in upper-limb rehabilitation as the prototype was considered to be generally well designed and capable of delivering reaching task therapy. The next stage of this project is to proceed to clinical trials with stroke patients.</p