Internet of Things-based telemonitoring rehabilitation system for knee injuries

Bulletin of Electrical Engineering and Informatics
Vol. 9, No. 6, December 2020, pp. 2658~2666
ISSN: 2302-9285, DOI: 10.11591/eei.v9i6.2428  2658
Journal homepage: http://beei.org
Internet of Things-based telemonitoring rehabilitation system
for knee injuries
Muheeb Musaed M. Al-Omri1
, Nayef Abdulwahab Mohammed Alduais2
, Mohamad Nazib Adon3
,
Abdul-Malik H. Y. Saad4
, Antar Shaddad H. Abdul-Qawy5
, Tole Sutikno6
1,3
Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia
2
Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia
4
School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Penang, Malaysia
5
Science & Information Technology Dept., Faculty of Science, SUMAIT University, Zanzibar, Tanzania
6
Department of Electrical Engineering, Universitas Ahmad Dahlan, Yogyakarta, Indonesia
6
Embedded System and Power Electronics Research Group (ESPERG), Yogyakarta, Indonesia
Article Info ABSTRACT
Article history:
Received Mar 7, 2020
Revised Jul 19, 2020
Accepted Aug 4, 2020
Rehabilitation engineering, as one of the active research areas in biomedical
science, needs further investigations and improvements. The process of
rehabilitation, whether after a stroke, ligament, or accident-related injuries, is
commonly based on clinical assessment tools, which can be executed, either
by self-reported (home-based) treatment or through observer-rated therapy.
However, people with reduced mobility (e.g., stroke, surgical, and ligament
patients) can benefit from rehabilitation programs only if effective and
appropriate assistive tools are provided. In this paper, a new Internet of
Things (IoT)-based telemonitoring system is introduced for knee injuries’
rehabilitation (Knee-Rehab). The proposed system is a real-time rehabilitation and
monitoring framework designed to be a portable, home-based, and online-based
instrument comprised of bio-mechanical, bio-instrumentation and IoT-based
elements. The system helps patients to rest at home after surgeries or physical
treatment, do their rehab-exercises, and receive suggestions form their advisors,
which gain the ability to monitor the situation over the exercising time and
propose necessary medication/activities to be followed by the patients
accordingly, based on their current status. The experimental measurements show
the high accuracy achieved by the developed system in terms of monitored knee
joint angle, where the maximum error is 3.5° compared to manual goniometer
measurements.
Keywords:
Biomedical
Goniometer measurements
Internet of Things
Knee rehabilitation
Reduced mobility
This is an open access article under the CC BY-SA license.
Corresponding Author:
Nayef Abdulwahab Mohammed Alduais,
Faculty of Computer Science and Information Technology,
Universiti Tun Hussein Onn Malaysia,
86400 Parit Raja, Batu Pahat Johor, Malaysia.
Email: nayef@uthm.edu.my
1. INTRODUCTION
The purpose of rehabilitation is to restore back the normal physical movement of any part of
the human body that has been lost due to a stroke or any accident-related injury [1], [2]. The process depends
on using clinical assessment tools and can be done either as a home-based self-treatment or through an observer-rated
method at the rehabilitation center [3]. The later can be time-consuming and cost-intensive. The patients are
required to do their exercises at the rehabilitation center frequently under observations over the treatment
time. In addition, patients living in remote and rural areas may have limited access and face difficulties in

Bulletin of Electr Eng & Inf ISSN: 2302-9285 
Internet of Things-based telemonitoring rehabilitation system for… (Muheeb Musaed M. Al-Omri)
2659
attending rehabilitation centers, which advise them to come back and forth periodically. Further, problems
encountered at the time of occupational therapy interventions, quality of the material itself, and the level of
patient ability to recover, may impose an extra cost. Thus, such limitations of the observer-based
rehabilitation model need to be addressed and considered when developing new rehabilitation tools, so as to
provide time-flexible, cost-effective and affordable rehabilitation assistive product.
The home-based rehabilitation system is an exceptional solution to overcome such limitations. It allows
patients to rest at home after surgeries or physical treatments, do the rehab-exercises as a self-treatment, and follow
instructions given by their remote observers immediately. The Internet of Things (IoT) is a communication
paradigm that envisions total connectivity with objects of everyday life and is an integral part of the internet
infrastructures [4], [5]. In recent years, the emergence of the IoT has brought about tremendous technological
changes [6]-[9], especially with the current trend towards smart cities and smart buildings. Some examples
are the smart wearable tracking kid’s system in a smart building, and human motion wearable tracking
system for healthcare application [10]. Rehabilitation and elderly monitoring for active aging can benefit from
IoT capabilities, particularly, for in-home treatments [11]-[13]. The observer can monitor the situation
continuously in real-time, propose necessary medications, and suggest appropriate activities based on the patients'
conditions and limitations. Athletes, for example, may encounter several injuries including, knee fracture, anterior
cruciate ligament (ACL), posterior cruciate ligament (PCL), or any other type of ligament injuries [14].
Therefore, rehabilitation tools should be capable to help in recovering from such problems, reducing
the muscle stiffness, and supporting a range of motion (for example, hip and knee flexion) [15].
In the last few years, a number of IoT-based solutions for knee monitoring systems have been
suggested. Golgouneh et al. [16] have proposed a lighter, portable, and adjustable continuous passive motion
(CPM) machine with a user-friendly interface. An android-based application is used for interaction between
users and the introduced tool so that data can be stored and recorded in the patient's smartphone via
Bluetooth communication. Thus, the patient can use this new machine for adjusting her/his leg movement
without any external help compared to the traditional CPM machine which is bulky, heavy, and expensive.
In [17], a mobile application is developed by Gay et al., as a monitoring method that keeps tracking of the
patient's progress, reports the current status of the rehabilitation process to the health professional, and
provides patients with immediate feedback accordingly. The inconvenience of this platform is that the
patient/user needs an actual description of the pain amount based on feeling. In addition, their evaluation
procedure does not refer to any practical measurements, implying an inaccurate outcome of the study.
In [18], Bonnet et al. developed a pseudo-online 3D estimation scheme of the hip, ankle, and knee joint
angles of patients through an automated system. Nevertheless, the work is limited to five types of
rehabilitation exercises, which should be performed in a standing mode. Also, the results of measurement are
not accurate in practice as all the values should be calculated by the proposed algorithm itself. In [19],
Tu et al. designed a new system for lower limb rehabilitation, hip-knee-ankle. They used an integrated
system, which composed of low-cost hip-knee exoskeleton prototype, functional electrical stimulation (FES),
and chattering mitigation robust variable control (CRVC) algorithm. Their aim was to minimize the cost and
design complexity of the hip-knee-ankle exoskeleton. However, such a system seems to be complex for patients'
clinical treatment due to the huge size and bulky device. The authors used more than a technique to produce
experimental results. Ferriero et al. [20] presented a comparative study of two different methods of knee
angle measurements: DrGoniometer (DrG) and Universal Goniometer (UG). The study has been done to
evaluate the reliability of a developed application based on a smartphone, and is used for joint goniometry
assessment of clinical rehabilitation. However, the angle measurement process did not include a sensing unit, meaning
that the measurement is not practical. In [21]-[29], wireless surface electrical stimulator systems have developed
to measure the knee angle using wearable sensor technology, in which they used a pair of flex sensors
attached to a supportive cloth worn by the patient. However, the measurement process in their study needs
a physician’s attendance to help the patients on exercising required activities. In addition, the dislocation
of the patient leg during the rehab-exercise may affect the patient's healthcare and injured knee healing
process [21]. People with mobility limitations can benefit from rehabilitation programs, particularly, if an
appropriate rate of medicine is provided for the patient after surgery, stroke, or ligament injuries. Therefore, in this
paper, we develop a portable home-based knee rehabilitation tool along with an online monitoring system. Our
innovative apparatus integrates biomechanical, bioinstrumentation and IoT-based techniques. The aim
is to introduce an accurate, reliable, and flexible mechanism for effective patient recovery compared to the
traditional systems.
2. REHAB-SYSTEM ARCHITECTURE
In this section, a detailed description of the designed architecture of the Knee-Rehab system is provided, and
each subsystem is critically analyzed. The Knee-Rehab system contains nine individual parts/subsystems attached

 ISSN: 2302-9285
Bulletin of Electr Eng & Inf, Vol. 9, No. 6, December 2020 : 2658 – 2666
2660
together to form the desired model. For each subsystem, we discuss six main factors (including technologies,
dimensions, cost, performance, safety, materials, and competitive setting), which impact its functionality.
The designing process is achieved as in the following steps. First, a convenient and user-comfortable prototype has
been created through hand-drawing, while considering all the major parameters such as height, weight, strength,
adjustability, and sustainability. Then, SolidWork software has been used to sketch the created draw in order to
finalize mechanical tools to be used by humans. The designed model is shown in Figure 1. This model, then, is
converted into a 3-D view from the different sides as shown in Figure 2.
(a) (b)
(c) (d)
(e) (f)
Figure 1. The designed model: where (a) Illustrates the system design as a full piece and shows the
diameter between sides, (b), (c) and (d) Show the hole place basic design with a slider and its measurements,
(e) and (f) Show basic sketches of all the sides, along with measurements of all the details included

2661
(a) (b) (c)
(d) (e) (f)
Figure 2. The 3-D side views from different angles: (a), (b) and (c) Show the top view of chassis,
(d) and (e) Show the back-bottom view, while (f) shows the side view
The design attributes, such as size, length, and width of the frame, are taken based on appropriate
dimensions to facilitate activities of knee rehabilitation. Also, the type and quality of materials have been taken
into account. We have done necessary calculation to choose different types of materials based on the patient
force as well as the strength and weight of the material itself. Thus, all the system parts (such as chassis) were
made of strong and lightweight stuff that can withstand heavy-weight, force, and pressure imposed by
the patient when using Knee-Rehab. At the same time, the system can be easily carried by the rehab-user.
Figure 3 shows the system block-diagram, which includes a wooden chassis, two units of flexible sensors
(FS1 and FS2), and a microcontroller unit (Arduino UNO) connected to a Wi-Fi shield. The microcontroller unit
reads the data of knee joint movement using flex sensors, and sends this data through WiFi module to
the online Thikspeek cloud. The process starts at the hardware parts, in which the chassis is used to train
the patient, the Arduino reads (i) the bending angle from the flex sensors in terms of degree, and
(ii) the number of patient movements per round of activity from LDR sensor, and, then, data is sent to
Thikspeek on internet through WiFi unit. Thikspeek online platform shows the whole operations of the knee
monitoring rehabilitation system, which allows physicians to read data and analyze patient’s improvement
so as to make the right decision in real-time. The patients, also, can see their improvement level in the
LCD display.
Figure 3. The block diagram of the proposed system

 ISSN: 2302-9285
2662
The system includes two units of 4.5-inches flex sensors placed on a supportive cloth worn by
the patient under testing progress as shown in Figure 4, and a light dependent resistor (LDR) sensor placed
on the mechanical chassis. A portable and adjustable mechanical slider made of wooden material is
connected to the light-sensitive LDR sensor to count the number of moves per active round. In addition,
a resistance band should be held by the patient for limb movements. When the body joint moves or flexes,
the resistivity of the flexure sensor changes, which can be mapped to the angle between the joints through
post-processing. In some applications, such as monitoring the progress of body joints after surgery, precise
angles are less important than consistency of measurements over time. Further, the change and development
of joint flexion are more critical than the accuracy of the joint angle. We model the behavior of joint motion
over time and use this model in a real-time rehabilitation monitoring system to obtain measurements from
wearable sensors. Thinkspeak platform has been used for online data monitoring in the proposed system.
Figure 4. The positions of two flex sensors
3. RESULTS AND DISCUSSION
The final prototype for the designed system is shown in Figure 5. On the Figure 5(a), the figure shows
a well-equipped system with all the developed parts, while on the Figure 5(b) it shows how the patient is using
the device. For evaluating the designed prototype, several tests have been conducted on normal users as well
as patients. At the first stage, the developed system has been tested on four normal (healthy) users to ensure
the functionality of the entire system before sending it to the clinic for actual use by patients. Figure 6 shows
the results (knee angle measurements) from using the prototype by normal users with different activities. Table 1
illustrates the maximum, minimum and average bending values measured from FS1 and FS2 for each user. The
average difference between the readings data from FS1 and FS2 for User1, User2, User3 and User4 are 10.6°,
0.63324°, 0.904°, and 9.3°, respectively. The recorded large differences show clearly the purpose of using
two flex sensors since they are not too accurate. By taking the average of the data read from FS1 and FS2,
the error is significantly reduced into less than 4°, which is considered very small value as it represents an
invisible bend in the knee. Therefore, the proposed system can be used efficiently for monitoring patients’
condition and provides sufficient motivation. In addition, the system can be used to assess rehabilitation progress.
(a) (b)
Figure 5. The prototype under using condition, (a) Equipped system, (b) Patient is using the device
Furthermore, additional experimental tests have been conducted to ensure the prototype
functionality when applied to actual patients. In this experiment, we have performed three tests on four
patients. The flexible sensors were connected to the Arduino UNO to collect the measured data. Table 2

2663
shows the results for each individual test and user in term of average measured bending angles. To compute
the exact measurement error, we compared the obtained results with that of using goniometer manual
measurements. The manual results using goniometer are tabulated in Table 2 for each individual test.
From this table, it is clear that the limits of bending are almost between 40 to 70 degrees. This illustrates that
the tests have been done to those who do not have serious injuries in their knees. It, also, indicates the ability
of the measurement system to read the bending angle in different positions with closer values, and how
the integrated sensors are working together with a minimum recorded error, i.e. 3.5° at maximum. According
to the results in Table 2, it can be seen that the average angle values for the four patients are different. This is
because that each user is different in size and shape. Hence, this proves that the developed device is so
flexible that it can be worn and used by anyone.
Figure 6. Results of testing the integrated system with a normal user
Table 1. The obtained data from FS1 and FS2 sensors for different users’ activities
User1 User2 User3 User4
FS1 FS2 FS1 FS2 FS1 FS2 FS1 FS2
Maximum bend (°) 100 97 87.6 95.76 92.04 93.42 102 92
Minimum bend (°) 22 29 14.2 8.9 31.5 23.4 1 1
Average bend (°) 60.9 50.32 50.3747 51.01 55.91 55 56 47.148
Average readings difference (°) 10.6 0.63324 0.904 9.3
Table 2. Results obtained from the designed Knee-Rehab assessment device and goniometer
Patient 1 Patient 2 Patient 3 Patient 4
Test 1 average 41.7° 49.45° 48.63° 48.37°
Test 2 average 51.09° 54.9° 59.71° 52.92°
Test 3 average 58.78° 69.71° 69.35° 66.02°
Average bending angle for three tests 50.523° 58.02° 59.23° 55.77°
Goniometer manual measurement 53° 55° 60° 57°
Besides its core functions, which are patient rehabilitation, and measuring the knee bending angle,
our introduced system can also work as a monitoring device. The data from the microcontroller can be sent
online to therapists. For users themselves, they can monitor their improvements during the physiotherapy
session through the data appeared on the LCD display as a real-time feedback. This gives them an empirical
benchmark so that they know the level of improvement at which they reached, either they are in a good condition or
not. In Figure 7, we can see three different values on the LCD display. In the first line, the value represents
the movement number, which is the result of the LDR sensor; while in the second line, the values, A1
and A2, are the measures of bending angles from FS1 and FS2 sensors, respectively. Figure 8 shows
the online monitoring system using ThinkSpeak platform. The monitored data as shown has been collected,
processed, and transferred by sensors, processor, and Wi-Fi module respectively. This cloud platform allows
specialists to monitor the results at any time and make the appropriate decision based on the analyzed data.
All the collected data has been used as an indicator to evaluate the progress of knee rehabilitation exercise.
To do that, three tests have been performed on three different patients and the improvement amount is
computed using (1).

 ISSN: 2302-9285
2664
( ) (1)
where Abd is the average bending improvement in degree, LAt is the average bending on the last test, and F
At is the average bending on the first test.
Table 3 shows the calculated average of patient recovery. It is obvious that the ability of recovery
is improved after a short exercise. In addition, the system has an acceptable accuracy to be used as a knee
rehabilitation device. By comparing the results obtained from the manual goniometer to that measured by
the developed system, as shown in Table 2, it is observed that the sensitivity of using two flex sensors is high
and the error is tiny. The maximum error was only ±3.5° of the actual angle.
(a) (b)
Figure 7. System output on LCD display shows the reading values from the two flex sensors while,
(a) The knee is bend, (b) Straight
Figure 8. Online monitoring system using ThinkSpeak platform
Table 3. The average of patient recovery
User Patient 1 Patient 2 Patient 3 Patient 4
Average recovery over time in (°) 17.08° 20.26° 20.72° 19.63°
4. CONCLUSION
In this paper, we have developed an IoT-based knee rehabilitation and monitoring system that
benefits patients to recover from consequences of various knee injuries. The major goal is to make
the rehabilitation process more accurate, flexible, and effective in terms of time and cost. The system consists of
a number of subparts (i.e., bio-mechanical, bio-instrumentation, and IoT-based parts), integrated together to
form a home-based portable assistive device. Several experiments have been conducted for evaluating
the integrated system with different patients, having different situations and activities. The measurement values
indicate that our system is able to accurately read the bending angle in various placements with a maximum error
value equal to 3.5° compared to the manual goniometer measurements. The experimental outcomes show
satisfying results that denote the superiority of our rehabilitation system compared to conventional models.
This implies that our provided rehabilitation system is reliable and efficient enough to be used by different
users under any conditions. This helps patients to do their rehab-exercises at home while receiving feedback from
their advisors immediately. The advisors can remotely monitor the patient rehabilitation process in real-time
through the introduced monitoring system, which uses ThinkSpeak online cloud. They can read the collected

2665
data sent form rehabilitation device to the online monitoring system, analyze this data, and send their
recommendations to be followed by patients based on his/her current condition. The proposed system overcomes
the limitations of the observer-rated therapy model, in which the treatment is done at the rehabilitation center.
Thus, the patients can be afforded a portable, lightweight, reliable, and easy to use rehabilitation tool while
keeping in contact with their observers continuously during the treatment period.
REFERENCES
[1] Reistetter, T., Hong, I., Mallinson, T., Anderson, C., Kuo, Y. F., Ottenbacher, K., & Goodwin, J., “Instrumental
Variable Analysis for Stroke Rehabilitation Outcomes Comparing Inpatient Rehabilitation and Skilled-Nursing
Facilities,” American Journal of Occupational Therapy, vol. 73, 2019.
[2] Buetow, S. A., Martínez-Martín, P., McCormack, B., “Ultrabilitation: beyond recovery-oriented rehabilitation,”
Disability and rehabilitation, vol. 41, no. 6, pp. 740-745, 2017.
[3] Thomas, R. J., Beatty, A. L., Beckie, T. M., Brewer, L. C., Brown, T. M., Forman, D. E., and Sanderson, B. K.,
“Home-Based Cardiac Rehabilitation: A Scientific Statement From the American Association of Cardiovascular
and Pulmonary Rehabilitation, the American Heart Association, and the American College of Cardiology,” Journal
of the American College of Cardiology, vol. 74, no. 1, pp. 133-153, 2019.
[4] Alduais, Nayef Abdulwahab Mohammed, Jiwa Abdullah, and Ansar Jamil, "RDCM: An Efficient Real-Time Data
Collection Model for IoT/WSN Edge With Multivariate Sensors," in IEEE Access, vol. 7, pp. 89063-89082, 2019.
[5] Alduais, N. A. M., J. Abdullah, and A. Jamil, "APRS: adaptive real-time payload data reduction scheme for
IoT/WSN sensor board with multivariate sensors," International Journal of Sensor Networks, vol. 28, no. 4,
pp. 211-229, 2018.
[6] Gupta, D., Parikh, A., & Swarnalatha, R. “Integrated healthcare monitoring device for obese adults using internet
of things (IoT),” International Journal of Electrical & Computer Engineering (IJECE), vol. 10, no. 2,
pp. 1239-1247, 2020.
[7] Bhagchandani, K., & Augustine, D. P., “IoT based heart monitoring and alerting system with cloud computing and
managing the traffic for an ambulance in India,” International Journal of Electrical and Computer Engineering
(IJECE), vol. 9, no. 6, pp. 5068-5074, 2019.
[8] Ibrahim, S. N., Basri, A. H., & Asnawi, A. L., “Development of web-based surveillance system for Internet of
Things (IoT) application,” Bulletin of Electrical Engineering and Informatics, vol. 8, no.3, pp. 1108-1116, 2019.
[9] Abdullahi, S. I., Habaebi, M. H., & Abd Malik, N., “Intelligent flood disaster warning on the fly: developing IoT-
based management platform and using 2-class neural network to predict flood status,” Bulletin of Electrical
Engineering and Informatics, vol. 8, no. 2, pp. 706-717, 2019.
[10] Alduais, N. A. M., I. Abdullah, and A. Jamil, "An efficient data collection algorithm for wearable/mobile tracking
system in IoT/WSN," In 2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar
(EECCIS), Batu, East Java, Indonesia, pp. 250-254, 2018.
[11] Peng, Tongqian, "A novel motion detecting strategy for rehabilitation in smart home," Computer Communications,
vol. 150, pp. 687-695, 2020.
[12] Bisio, A. Delfino, F. Lavagetto and A. Sciarrone, "Enabling IoT for In-Home Rehabilitation: Accelerometer Signals
Classification Methods for Activity and Movement Recognition," in IEEE Internet of Things Journal, vol. 4, no. 1,
pp. 135-146, 2017.
[13] Zhang, Wenlong & Tomizuka, Masayoshi & Byl, Nancy, "A Wireless Human Motion Monitoring System for Smart
Rehabilitation," Journal of Dynamic Systems, Measurement, and Control. vol.138, no. 11, pp. 10041-10049, 2016.
[14] C. L. Ardern, G. R. Ekas, H. Grindem, et. al, “2018 International Olympic Committee Consensus Statement on
Prevention, Diagnosis, and Management of Pediatric Anterior Cruciate Ligament Injuries,” British Journal of
Sports Medicine, vol. 52, no. 7, 2018.
[15] Rabin, A., Portnoy, S., & Kozol, Z., “The association of ankle dorsiflexion range of motion with hip and knee
kinematics during the lateral step-down test,” Journal Of Orthopaedic & Sports Physical Therapy, vol. 46, no. 11,
pp. 1002-1009, 2016.
[16] A. Golgouneh, A. Bamshad, B. Tarvirdizadeh and F. Tajdari, "Design of a new, light and portable mechanism for
knee CPM machine with a user-friendly interface," 2016 Artificial Intelligence and Robotics (IRANOPEN), Qazvin,
pp. 103-108, 2016.
[17] Gay, V., Leijdekkers, P., & Barin, E., “A mobile rehabilitation application for the remote monitoring of cardiac
patients after a heart attack or a coronary bypass surgery,” In Proceedings of the 2nd international conference on
pervasive technologies related to assistive environments, no. 21, pp. 1-7, 2009.
[18] Bonnet, V., Mazza, C., Fraisse, P., & Cappozzo, A., “Real-time estimate of body kinematics during a planar squat
task using a single inertial measurement unit,” in IEEE Transactions on Biomedical Engineering, vol. 60, no. 7,
pp. 1920-1926, 2013.
[19] X. Tu, J. Huang, M. Ieee, J. He, and S. Member, “Leg Hybrid Rehabilitation based on Hip-Knee Exoskeleton and
Ankle motion Induced by FES,” 2016 International Conference on Advanced Robotics and Mechatronics
(ICARM), Macau, pp. 237-242, 2016.
[20] Ferriero, G., Vercelli, S., Sartorio, F., Lasa, et. al, “Reliability of a smartphone-based goniometer for knee joint
goniometry,” International journal of rehabilitation research, vol. 36, no. 2, pp. 146-151, 2013.

 ISSN: 2302-9285
2666
[21] Masdar, Aizan, B. S. K. K. Ibrahim, Dirman Hanafi, M. Mahadi Abdul Jamil, and K. A. A. Rahman, "Knee joint
angle measurement system using gyroscope and flex-sensors for rehabilitation," In The 6th 2013 Biomedical
Engineering International Conference, Amphur Muang, pp. 1-4, 2013.
[22] Miura N., Watanabe T., Akasaka K., Suzuki T., "A clinical trial of a prototype of wireless surface fes rehabilitation
system in foot drop correction," 2011 Annual International Conference of the IEEE Engineering in Medicine and
Biology Society, Boston, MA, pp. 5461-5464, 2011.
[23] Matjacić Z., Munih M., Bajd T., Kralj A., Benko H., Obreza P., "Wireless control of functional electrical
stimulation systems,” Artif Organs, vol. 21, no.3, pp. 197-200, 2008.
[24] Winfried M. B., Rakos M. M., Hermann C. H., Rafolt L. D., Stefan M. R., Schmutterer S. C., Michael E. U.,
Kern R. H., "The Vienna Functional Electrical Stimulation System for Restoration of Walking Functions in Spastic
Paraplegia,” Artif Organs, vol. 26, no.3, pp. 224-227, 2002.
[25] Fonseca L. O., Bó A. P. L., Guimarães J. A., Gutierrez M. E., Fachin‐Martins E., "Cadence Tracking and
Disturbance Rejection in Functional Electrical Stimulation Cycling for Paraplegic Subjects: A Case Study,” Artif
Organs. vol. 41, no.11, pp. E185-E195, 2017.
[26] Melo P. L., Silva M. T., Newman J. M. D., "A Microcontroller Platform for the Rapid Prototyping of Functional
Electrical Stimulation‐Based Gait Neuroprostheses,” Artif Organs, vol. 39, no.5, pp. E56-E66, 2015.
[27] Donovan‐Hall M. K., Bridget J. B., Ellis‐Hill D. C., Rushton D., "The Views of People with Spinal Cord Injury
About the Use of Functional Electrical Stimulation,” Artif Organs, vol. 35, no.3, pp. 204-211, 2011.
[28] Pečlin P., Rozman J., Krajnik J., Ribarič S., "Evaluation of the Efficacy and Robustness of a Second-Generation
Implantable Stimulator in a Patient with Hemiplegia During 20 Years of Functional Electrical Stimulation of the
Common Peroneal Nerve,” Artif Organs, vol. 40, no.11, pp. 1085-1091, 2016.
[29] Kneisz L., Unger E., Lanmüller H., Mayr W., "In Vitro Testing of an Implantable Wireless Telemetry System for
Long‐Term Electromyography Recordings in Large Animals,” Artif Organs, vol. 39, no.10, pp. 897-902, 2015.

Internet of Things-based telemonitoring rehabilitation system for knee injuries

More Related Content

What's hot (19)

Similar to Internet of Things-based telemonitoring rehabilitation system for knee injuries (20)

More from journalBEEI (20)

Recently uploaded (20)

Internet of Things-based telemonitoring rehabilitation system for knee injuries