Posts Tagged rehabilitation exercises
Two mandatory conditions in the development of tele-rehabilitation platforms are:
- (i) being based on affordable technologies and
- (ii) ensuring the patient is performing the exercises correctly.
To do so, the present study proposes a cognitive algorithm based on a Hidden Markov Model (HMM) approach to assess in real-time the quality of a human movement recorded through a low-cost motion capture device.
The assessment of the correctness of the exercises, which includes the detection of multiple undesirable compensatory movements, shows a very high accuracy (the average performance = 97%). In addition, the proposed model shows a potential for providing the patients with real-time feedback on their performance (up to five times a second).
A certain limitation of the model occurs for the compensatory movements characterized by an absence of translational motion of the centre of mass (17% of misclassifications). In this situation, additional features are required to properly assess the quality of the therapeutic exercise.
Rehabilitation exercises are those exercises that help in
improving joint and muscle function, helping people stand,
balance, walked. But these exercises work if done regularly
and done as proposed by the therapist. Sometimes the patients
has problems like scheduling their daily tasks, their
commitment for doing those exercises, and some other
difficultiessimilar to this. Thus failing to do the
movements and get benefit from the exercises. This paper
proposes a system that provides an intuitive way for
rehabilitation. This system contains use of pervasive health
technologies for addressing the over difficulty. The system
would provide a graphical interfacethat would help the
physiotherapist to create exercises in 3Denvironment, wherein
he would be animating a humanoid to showhow the exercise
is to be done.This would show to be more intuitive to patients
rather thanon paper. The system would also let therapist to
monitor patient while he/she is exercising.
Physical therapy cures injuries and promotes movements of
injured body parts by examining the patient’s body, diagnosing
the patient and then treating the patient using mechanical
force and movements which is carried out by physical
therapists (also known as physiotherapist). Analysis aims to
extend, and restore maximum functional ability throughout
life action uses the patient history and the physical
examination to find out what really the patient needs to do, to
restore the mobility of the injured muscle. Physical therapy
has many types of specialities:
• Orthopaedic and
With the developing technology worldwide, people have
started using handheld devices to carry on their day-to-day
activities. With this advancement, the demand for pervasive
computing and 3D visualization of images and videos has
been raised. The area this paper focuses on is a tele-medicine
and tele-therapy system for helpingthe patient to get the help
of the doctors at their home. When a patient undergoes a
surgery, he may need a physiotherapy session to regain his/her
muscle functionality. For this purpose the patient may need to
take one-to-one session with the physiotherapist. The patient
requires to do these sessions again and again till he/she
regains the muscle functionality. Much man power, resources
and time is need to do all this.
In today’s life, it may be impossible for a patient to get
sometime from the busy schedule and go for these one-to-one
sessions.Also there are elderly people who get surgery but
aren’t able to goto the therapist for every day sessions, also
there are people that live inremote location and due to this
they are not able to get good healthcare services. To resolve
such problems we will be developing a telephysiotherapysystem,
which would help to cater patients at
theirdoorstep. The system would be developed in two stages.
In the firststage the doctor will be provided with an interface
where he/shecan animate a humanoid with the exercise the
patient needs to do.
This would be done using the mobile device the therapist
would behaving. After creating this animation, the therapist
would be ableto record the animation and send the animation
to the appropriatepatient. In the second stage we would be
using sensors and otherdevices to monitor the patient
exercises(If the first stage proves tobe successful).
As stated in the first stage, the therapist would be able to
create3D animations, for which we can use the 3D computer
graphicalrendering on mobile devices. This 3D visualization
would be moreintuitive than the printed paper counterpart.
Thus, it is hoped thatthis would help to improve the patient
health in less costly way.
[ARTICLE] Rehabilitation exercise assessment using inertial sensors: a cross-sectional analytical study
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.
The complete article is available as a provisional PDF. The fully formatted PDF and HTML versions are in production.