Posts Tagged Inertial sensors

[Conference paper] Kushkalla: A Web-Based Platform to Improve Functional Movement Rehabilitation – Full Text


Telerehabilitation is a growing alternative to traditional face-to-face therapy, which uses technological solutions to cover rehabilitation care in both clinical centers and in-home programs. However, the current telerehabilitation systems are limited to deliver a set of exercise programs for some specific locomotor disability, without including tools that allow a quantitative analysis of the rehabilitation progress, in real-time, as well as the medical condition of patients. This paper presents the design and development of a novel web-based platform, named “Kushkalla”, that allows to perform movement assessment for creating personalized home-based therapy routines, integrating hardware and software tools for a quantitative analysis of locomotor movements based on motion capture, preprocessing, monitoring, visualization, storage and analysis, in real-time. The platform combines two motion capture strategies, the Kinect-based and IMU-based motion capture. In addition, a set of 2D and 3D graphical models, virtual environments, based on WebGL technology, and videoconference module are included to allow the interaction between user and clinician for enhancing the capability of the clinician to direct rehabilitation therapies.


According to the World Health Organization, at least 15% of world people could present musculoskeletal disabilities, which present difficulties to access appropriate management even in diagnosis, treatment or follow-up stages. Particularly, it is estimated that between 76% and 85% of disabled people have not accessed to treatment programs in developing countries [17]. Conventionally, when a musculoskeletal disability is diagnosed, a clinical specialist designs a specific functional rehabilitation program, according to the analysis of the strength, flexibility and other biomechanical aspects of the patient; then, a team of therapists is responsible for its execution and follow-up. Both diagnosis and follow-up require quantifying those biomechanical aspects in order to guarantee that the designed program is suitable for the patient. This workflow demands an important number of therapists and technologies, such as strength platforms, to ensure the quality of the rehabilitation program. Additionally, the patient location could be a major obstacle for this purpose. This is the case of some rehabilitation programs to restore functional movements of elderly people, which are constantly suffering locomotor impairment caused by aging. Thus, functional movement rehabilitation programs evaluate the movement patterns from each patient to establish what parts of the human body may be treated. An improper movement pattern or imbalances throughout the human body allow determining postural and motor issues, which are used to develop different rehabilitation programs by the therapist. Therefore, functional movement rehabilitation programs are able to rehabilitate the human body that is weak, tight or unbalance by using a combination of functional movement correction and classic rehabilitation exercises.

Recently, telerehabilitation has emerged as an alternative that allows to perform functional movement rehabilitation activities from the comfort of the patient location, which are monitored by the physician from the specialized medical center [14]. This is possible by the use of the Internet and emerging technologies such as inertial sensors, optical motion capture devices, robots, virtual reality environments, among others [4]. In general, telerehabilitation strategies can be classified as: telepresence-based rehabilitation, which are supported by videoconference tools that allow a continuous communication between patient and physician [3]; robotic-based rehabilitation, which uses autonomous robots or exoskeletons for guiding patient movements [7]; interactive-based rehabilitation, which uses interactive environments for motivating patient to perform exercises while playing [121521] and; rehabilitation based on a precision analysis, which provides movement analysis tools for supporting the physician decisions [11].

This paper describes the design and development of a novel web-based platform that integrates telepresence, interactive environments, and movement analysis tools, for providing the technology to carry out functional movement assessment and to create personalized home-based therapy routines. The proposed Web-based platform was developed on a service-oriented architecture (SOA), a client/server software design approach in which an application consists of software services and software service consumers that are provided between software components through several network communication protocols [16]. It is composed of two main software parts: a client and a cloud server components. Additionally, two applications conform the client component: the patient application, and the physician application. The patient application includes a bimodal human motion capture module that allows to integrate both a wearable inertial sensor system and a depth camera sensor (Kinect); a visualization module provided with a virtual environment with an interactive interface in which patient can see in two 3D avatars how an exercise must be executed and how they execute it; and an assistance module provided with a videoconference tool and videotutorials about the platform. The Physician application includes an exercise visualization module, synchronized with the patient interface, in which real-time patient movements are displayed, and a motion analysis module, which displays graphically the movement measurements generated by the analysis of captured data. Finally, the server component, implemented as a software as a service cloud component that it includes a web-server, a websocket server, a webRTC (web with Real-Time Communications) server, and relational and non-relational databases.

This paper is organized as follows. The next section presents a brief summary of related works. In the Sect. 3 the main hardware/software components of the proposed platform are described. Section 4 presents a preliminary evaluation that shows the reliability of the proposed architecture and finally, Sect. 5 presents the conclusions and discuss the future work.[…]

Continue —>  Kushkalla: A Web-Based Platform to Improve Functional Movement Rehabilitation | SpringerLink

Fig. 1. General framework of Kushkalla: Telerehabilitation platform

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[Systematic Review] Integration of emerging motion capture technologies and videogames for human upper-limb telerehabilitation – Full Text PDF


Integrating emerging technologies has shown to have the potential to improve access to rehabilitation services and the adherence for physical therapy when they are applied into telemedicine environments.

This systematic review aims to explore telerehabilitation systems that use motion capture and video games for upper-limb rehabilitation purposes. Motion capture was focused on the information fusion from inertial sensors and other technologies. The search was limited to 2010-2013, from which 667 papers were obtained; afterwards,
duplicate papers were removed, thus, reducing the sample to 57 papers with full text availability. Finally, only 3 of them were selected by approaching the subject of this study.

We conclude that the fusion information from inertial sensors and other motion capture technologies appears to be a new tendency in remote monitoring of motor rehabilitation process. However, the combination of them with active video games in physiotherapy programs is only an emerging research area with promising results.

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[ARTICLE] Rehabilitation exercise assessment using inertial sensors: a cross-sectional analytical study

Abstract (provisional)

Background: Accurate assessments of adherence and exercise performance are required in order to ensure that patients adhere to and perform their rehabilitation exercises correctly within the home environment. Inertial sensors have previously been advocated as a means of achieving these requirements, by using them as an input to an exercise biofeedback system. This research sought to investigate whether inertial sensors, and in particular a single sensor, can accurately classify exercise performance in patients performing lower limb exercises for rehabilitation purposes.

Methods: Fifty-eight participants (19 male, 39 female, age: 53.9 +/- 8.5 years, height: 1.69 +/- 0.08 m, weight: 74.3 +/- 13.0 kg) performed ten repetitions of seven lower limb exercises (hip abduction, hip flexion, hip extension, knee extension, heel slide, straight leg raise, and inner range quadriceps). Three inertial sensor units, secured to the thigh, shin and foot of the leg being exercised, were used to acquire data during each exercise. Machine learning classification methods were applied to quantify the acquired data.

Results: The classification methods achieved relatively high accuracy at distinguishing between correct and incorrect performance of an exercise using three, two, or one sensor while moderate efficacy scores were also achieved by the classifier when attempting to classify the particular error in exercise performance. Results also illustrated that a reduction in the number of inertial sensor units employed has little effect on the overall efficacy results.

Conclusion: The results revealed that it is possible to classify lower limb exercise performance using inertial sensors with satisfactory levels of accuracy and reducing the number of sensors employed does not reduce the accuracy of the method.


more–> JNER | Abstract | Rehabilitation exercise assessment using inertial sensors: a cross-sectional analytical study.

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