Posts Tagged Motion sensing

[WEB SITE] Therapy Mouse

 

Therapy Mouse wearable sensor

  • Maintain patient’s interest during therapy
  • Support dual tasking while in LiteGait
  •  Control any software that uses a mouse
  • Small lightweight
  • Attach to any body part
  •  No fuss, no set up, no cameras!
  •  Entertaining
  •  Extend therapy to home
  •  Game software options available

The Therapy Mouse kit comes complete with the sensor,
the small hand held clicker, USB receiver and straps.

If you would like a free 2 week trial of the Therapy Mouse, at no cost to you, please fill out this form and we will contact you to arrange.

Product Specifications

Therapy Mouse Packages Price
Basic 
– use your own Android, PC or Mac and your own personal mouse controlled games

includes sensor, clicker, headband and wrist slapper
Purchase Therapy Mouse
Plus
– use your own Android compatible device

Includes Basic (see above)
Plus Flash drive with 5 Therapy Mouse training games
*Android-based*
Purchase Therapy Mouse
Deluxe 
– Android Tablet included (10-inch Astrotab)

Includes Plus – (see above)
Android tablet installed with 8 sample arcade games.
No additional computer required.
*Other platforms available, ex: PC Stick*

 

 

via Therapy Mouse | LiteGait.com

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[REVIEW] Interactive wearable systems for upper body rehabilitation: a systematic review – Full Text PDF

Abstract

Background: The development of interactive rehabilitation technologies which rely on wearable-sensing for upper body rehabilitation is attracting increasing research interest. This paper reviews related research with the aim: 1) To inventory and classify interactive wearable systems for movement and posture monitoring during upper body rehabilitation, regarding the sensing technology, system measurements and feedback conditions; 2) To gauge the wearability of the wearable systems; 3) To inventory the availability of clinical evidence supporting the effectiveness of related technologies.

Method: A systematic literature search was conducted in the following search engines: PubMed, ACM, Scopus and IEEE (January 2010–April 2016).

Results: Forty-five papers were included and discussed in a new cuboid taxonomy which consists of 3 dimensions: sensing technology, feedback modalities and system measurements. Wearable sensor systems were developed for persons in: 1) Neuro-rehabilitation: stroke (n = 21), spinal cord injury (n = 1), cerebral palsy (n = 2), Alzheimer (n = 1); 2) Musculoskeletal impairment: ligament rehabilitation (n = 1), arthritis (n = 1), frozen shoulder (n = 1), bones trauma (n = 1); 3) Others: chronic pulmonary obstructive disease (n = 1), chronic pain rehabilitation (n = 1) and other general rehabilitation (n = 14). Accelerometers and inertial measurement units (IMU) are the most frequently used technologies (84% of the papers). They are mostly used in multiple sensor configurations to measure upper limb kinematics and/or trunk posture. Sensors are placed mostly on the trunk, upper arm, the forearm, the wrist, and the finger. Typically sensors are attachable rather than embedded in wearable devices and garments; although studies that embed and integrate sensors are increasing in the last 4 years. 16 studies applied knowledge of result (KR) feedback, 14 studies applied knowledge of performance (KP) feedback and 15 studies applied both in various modalities. 16 studies have conducted their evaluation with patients and reported usability tests, while only three of them conducted clinical trials including one randomized clinical trial.

Conclusions: This review has shown that wearable systems are used mostly for the monitoring and provision of feedback on posture and upper extremity movements in stroke rehabilitation. The results indicated that accelerometers and IMUs are the most frequently used sensors, in most cases attached to the body through ad hoc contraptions for the purpose of improving range of motion and movement performance during upper body rehabilitation. Systems featuring sensors embedded in wearable appliances or garments are only beginning to emerge. Similarly, clinical evaluations are scarce and are further needed to provide evidence on effectiveness and pave the path towards implementation in clinical settings.

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[ARTICLE] Interactive wearable systems for upper body rehabilitation: a systematic review – Full Text

Fig. 4 Infographic of sensor placements

Abstract

Background

The development of interactive rehabilitation technologies which rely on wearable-sensing for upper body rehabilitation is attracting increasing research interest. This paper reviews related research with the aim: 1) To inventory and classify interactive wearable systems for movement and posture monitoring during upper body rehabilitation, regarding the sensing technology, system measurements and feedback conditions; 2) To gauge the wearability of the wearable systems; 3) To inventory the availability of clinical evidence supporting the effectiveness of related technologies.

Method

A systematic literature search was conducted in the following search engines: PubMed, ACM, Scopus and IEEE (January 2010–April 2016).

Results

Forty-five papers were included and discussed in a new cuboid taxonomy which consists of 3 dimensions: sensing technology, feedback modalities and system measurements. Wearable sensor systems were developed for persons in: 1) Neuro-rehabilitation: stroke (n = 21), spinal cord injury (n = 1), cerebral palsy (n = 2), Alzheimer (n = 1); 2) Musculoskeletal impairment: ligament rehabilitation (n = 1), arthritis (n = 1), frozen shoulder (n = 1), bones trauma (n = 1); 3) Others: chronic pulmonary obstructive disease (n = 1), chronic pain rehabilitation (n = 1) and other general rehabilitation (n = 14). Accelerometers and inertial measurement units (IMU) are the most frequently used technologies (84% of the papers). They are mostly used in multiple sensor configurations to measure upper limb kinematics and/or trunk posture. Sensors are placed mostly on the trunk, upper arm, the forearm, the wrist, and the finger. Typically sensors are attachable rather than embedded in wearable devices and garments; although studies that embed and integrate sensors are increasing in the last 4 years. 16 studies applied knowledge of result (KR) feedback, 14 studies applied knowledge of performance (KP) feedback and 15 studies applied both in various modalities. 16 studies have conducted their evaluation with patients and reported usability tests, while only three of them conducted clinical trials including one randomized clinical trial.

Conclusions

This review has shown that wearable systems are used mostly for the monitoring and provision of feedback on posture and upper extremity movements in stroke rehabilitation. The results indicated that accelerometers and IMUs are the most frequently used sensors, in most cases attached to the body through ad hoc contraptions for the purpose of improving range of motion and movement performance during upper body rehabilitation. Systems featuring sensors embedded in wearable appliances or garments are only beginning to emerge. Similarly, clinical evaluations are scarce and are further needed to provide evidence on effectiveness and pave the path towards implementation in clinical settings.

Background

In musculoskeletal disorders, such as disorders of the neck-shoulder complex or osteoporosis, and in neurological disorders such as stroke, the integration of posture awareness of the upper trunk and shoulder complex as a stable basis for upper limb movement is an essential component of rehabilitation [1, 2, 3]. Therefore feedback on the posture of the trunk and shoulder complex and feedback on upper limb movement may be supportive of motor learning [4]. Although the pathological mechanisms of posture deviation during static conditions (standing, sitting) or during movement performance (upper limb activities, posture during gait) are quite different across the above mentioned patient populations the corresponding therapeutic approaches share an emphasis on increasing patient awareness of correct posture and movement patterns and the provision of corrective feedback during functional task execution. In all of the above patients, intrinsic feedback mechanisms that inform the patient (e.g. proprioceptive cues) are impaired [5, 6, 7] and extrinsic feedback is advocated to relearn correct joint positions/posture during movement. Traditionally extrinsic feedback is provided by a therapist, so this way of learning is very time consuming and difficult to carry out independently, e.g. during home exercises. Suitable rehabilitation technologies can potentially play an instrumental role in extending training opportunities and improving training quality.

Posture monitoring and correction technologies providing accurate, and reliable feedback, may support current rehabilitation activities [8, 9]. Ideally feedback is given continuously for users with low proficiency levels, and with fading frequency schedules for more advanced users [8]. In broad terms, there are five kinds of monitoring methods available: 1) traditional mechanical systems (e.g. goniometer); 2) optical motion recognition technologies [10]; 3) marker-less off body tracking systems like depth camera-based movement detection systems (e.g. Microsoft Kinect [11, 12]); 4) Robot-based solutions [13, 14]; 5) wearable sensor-based systems [4]. Recently, the miniaturization of devices, the evolution of sensing and body area network technologies [15, 16] has triggered the increasing influence of wearable rehabilitation technology, offering advantages over traditional rehabilitation services [17, 18], such as: low cost, flexible application, remote monitoring, comfort. Wearable sensing systems open up the possibility of independent training, the provision of feedback to the end-user as an active monitoring system, or even tele-rehabilitation.

A great number of wearable posture/motion monitoring systems for rehabilitation have been reported in literature in recent years, though very few have been used in clinical studies. Some studies introduce innovative wearable sensing technologies, e.g. Kortier et al. [19] developed a hand kinematics assessment glove based on attaching a flexible PCB structure on the finger that contains inertial and magnetic sensors. Tormene et al. [20] proposed monitoring trunk movements by applying a wearable conductive elastomer strain sensor. Studies like this are primarily concerned with demonstrating the accuracy and reliability of the technology they introduce. Another body of research concerns evaluations of existing rehabilitation technologies in terms of their validity. For example, Uswatte et al. [21] conducted a validation study of accelerometry for monitoring arm activity of stroke patients. Bailey et al. [22] proposed a study on a accelerometry-based methodology for the assessment of bilateral upper extremity activity. Lemmens et al. [23] report a proof of principle for recognizing complex upper extremity activities using body worn sensors.

There are a few examples of a literature that grows fast. The need arises to classify related works and identify promising trends or open challenges in order to guide future research. To address this need, there have been several reviews of research on wearable systems for rehabilitation, which take quite diverse perspectives on this vibrant field. An early review by Patel et al. [16] takes a very broad perspective that covers health and wellness, rehabilitation and even prevention, reviewing wearable and ambient technologies. Hadjidj et al. [24], provide an non-systematic review of literature on wireless sensor technologies focusing on technical requirements. Some studies focus on physical activity monitoring [25, 26] a technology domain that has had substantial growth and impact, but which is not specific to rehabilitation. Allet et al. [26] review wearable systems for monitoring mobility related activities in chronic diseases; this review covered mostly systems measuring general physical activity and found no works reaching the stage of clinical testing. Some studies provide an in-depth overview of movement measurement and analysis [27, 28, 29] technologies, though these are not necessarily integrated in rehabilitation systems and are usually still at the stage of proof of principle for a measurement technique. Vargas et al. [30] reviewed inertial sensors applied in human motion analysis, and concluded that inertial sensors can offer a task-specific accurate and reliable method for human motion studies. A couple of recent surveys [31, 32] have reviewed e-textile technologies applied in rehabilitation, though one of their main conclusions was to identify the distance separating the requirements for applying textiles to rehabilitation from the current state of the art. Also, they identify that the potential of providing feedback to patients based on textile sensing remains largely unexplored. Some studies concentrated specifically about how feedback influences therapy outcome [33, 34, 35], however the systems involved are not only wearable systems and all these reviews date 6 years or longer. Wang et al. [9] reviewed wearable posture monitoring technology studies from 2008 to 2013 for upper-extremity rehabilitation, yet unlike the present article, no systematic comparisons based on technology, system usability, feedback and clinical maturity were provided. In line with Fleury et al. [32] they found that only a few studies report the integration of wearable sensing in complete systems supporting feedback to patients, and very few of those have been tested by users with attention to the usability and wearability. Given the limited nature of that survey, such a conclusion was tentative calling for a systematic survey to gauge the state of the art in upper body rehabilitation technologies that integrate wearable sensors. The focus of the present survey is different regarding to the sensor type and placement, and rehabilitation objective. The present article contributes a different perspective to these surveys by critically reviewing and comparing systems comprising of feedback to support upper body rehabilitation with regard to their functionality and usability. In this review we focus on interactive wearable systems that provide feedback to end-users for rehabilitation. In addition, in order to review the latest and most innovative technological solutions that shed a light on the state of the art wearable solutions for rehabilitation, only articles published later than 2010 are considered.

The translation from a technical tool towards a clinically usable system is not straightforward. Prerequisites for therapists and patients to use technology supported rehabilitation systems are the easy-to-use character of the system, its added value to their habitual rehabilitation programs and its credibility. Besides, it is of major importance to design the system feedback as this positively influences motivation and self-efficacy [8]. Advanced technologies provide increasing possible forms of feedback and a growing number of studies used interactive wearable systems to motivate patients in the intensive and repetitive training.

As such, the purpose of this review is to provide an overview of interactive wearable systems for upper body rehabilitation. In particular, we aim to classify from the following aspects:

  1. To inventory and classify interactive wearable systems for movement and posture monitoring during upper body rehabilitation, regarding the sensing technology, system measurements and feedback conditions;

  2. To gauge the wearability of the wearable systems;
  3. To inventory the availability of clinical evidence supporting the effectiveness of related technologies.

Continue —> Interactive wearable systems for upper body rehabilitation: a systematic review | Journal of NeuroEngineering and Rehabilitation | Full Text

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[Thesis] Exploring In-Home Monitoring of Rehabilitation and Creating an Authoring Tool for Physical Therapists – Full Text PDF

Abstract

Physiotherapy is a key part of treatment for neurological and musculoskeletal disorders, which affect millions in the U.S. each year. Physical therapy treatments typically consist of an initial diagnostic session during which patients’ impairments are assessed and exercises are prescribed to improve the impaired functions. As part of the treatment program, exercises are often assigned to be performed at home daily. Patients return to the clinic weekly or biweekly for check-up visits during which the physical therapist reassesses their condition and makes further treatment decisions, including readjusting the exercise prescriptions.

Most physical therapists work in clinics or hospitals. When patients perform their exercises at home, physical therapists cannot supervise them and lack quantitative exercise data reflecting the patients’ exercise compliance and performance. Without this information, it is difficult for physical therapists to make informed decisions or treatment adjustments. To make informed decisions, physical therapists need to know how often patients exercise, the duration and/or repetitions of each session, exercise metrics such as the average velocities and ranges of motion for each exercise, patients’ symptom levels (e.g. pain or dizziness) before and after exercise, and what mistakes patients make.

In this thesis, I evaluate and work towards a solution to this problem. The growing ubiquity of mobile and wearable technology makes possible the development of “virtual rehabilitation assistants.” Using motion sensors such as accelerometers and gyroscopes that are embedded in a wearable device, the “assistant” can mediate between patients at home and physical therapists in the clinic. Its functions are to:

  • use motion sensors to record home exercise metrics for compliance and performance and report these metrics to physical therapists in real-time or periodically;
  • allow physical therapists and patients to quantify and see progress on a fine-grain level;
  • record symptom levels to further help physical therapists gauge the effectiveness of exercise prescriptions;
  • offer real-time mistake recognition and feedback to the patients during exercises;

One contribution of this thesis is an evaluation of the feasibility of this idea in real home settings. Because there has been little research on wearable virtual assistants in patient homes, there are many unanswered questions regarding their use and usefulness:

  • Q1. What patient in-home data could wearable virtual assistants gather to support physical therapy treatments?
  • Q2. Can patient data gathered by virtual assistants be useful to physical therapists?
  • Q3. How is this wearable in-home technology received by patients?

I sought to answer these questions by implementing and deploying a prototype called “SenseCap.” SenseCap is a small mobile device worn on a ball cap that monitors patients’ exercise movements and queries them about their symptoms. A technology probe study showed that the virtual assistant could gather important compliance, performance, and symptom data to assist physical therapists’ decision-making, and that this technology would be feasible and acceptable for in-home use by patients.

Another contribution of this thesis is the development of a tool to allow physical therapists to create and customize virtual assistants. With current technology, virtual assistants require engineering and programming efforts to design, implement, configure and deploy them. Because most physical therapists do not have access to an engineering team they and their patients would be unable to benefit from this technology. With the goal of making virtual assistants accessible to any physical therapist, I explored the following research questions:

  • Q4. Would a user-friendly rule-specification interface make it easy for physical therapists to specify correct and incorrect exercise movements directly to a computer? What are the limitations of this method of specifying exercise rules?
  • Q5. Is it possible to create a CAD-type authoring tool, based on a usable interface, that physical therapists could use to create their own customized virtual assistant for monitoring and coaching patients? What are the implementation details of such a system and the resulting virtual assistant?
  • Q6. What preferences do PTs have regarding the delivery of coaching feedback for patients?
  • Q7. What is the recognition accuracy of a virtual rehabilitation assistant created by this tool?

This dissertation research aims to improve our understanding of the barriers to rehabilitation that occur because of the invisibility of home exercise behavior, to lower these barriers by making it possible for patients to use a widely-available and easily-used wearable device that coaches and monitors them while they perform their exercises, and improve the ability of physical therapists to create an exercise regime for their patients and to learn what patients have done to perform these exercises. In doing so, treatment should be better suited to each patient and more successful.

To Continue —> Download Full Text PDF

 

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