... Vol 24 , no 5, pp 723 -730, 15 Apr 20 09 [20 ] A Tay, SC Yen, JZ Li et al., “Real-time Gait Monitoring for Parkinson Disease,” Int Conf on Control and Automation., pp 1796-1801, June 12- 14, 20 13 [21 ]... between Kinesia system assessments and clinical tremor scores in patients with essential tremor”, Movement Disorders, Vol 25 , no 12, pp 1938-1943, 15 Sep 20 10 109 [22 ] LaStayo PC, Wheeler DL, ”Reliability... of Singapore June 20 12 to May 20 13 InnovFest 20 14 by NUS Enterprise 14 to 16 April 20 14 106 Bibliography [1] Neil R Sims, Hakan Muyderman, “Mitochondria, oxidative metabolism and cell death in
4.3 Preliminary Testing Preliminary testing of the application was done on a healthy elderly test subject who stays with her caretaker. The aim of this test was to check the reliability of the system when it is left at a patient’s home and to investigate if the caretaker can help the patient to use the tele-rehabilitation system. During the initial set-up of the system, the caretaker was trained to operate the system and a therapist prescribed exercises for the subject. The system was left at the patient’s home for a week to collect data. It was reported that the system failed to capture data for some of the days. Improvements are being made to the system in response to the feedback gathered from this week of testing in order to make it more reliable and robust. The process of detecting if the system is set up properly is being automated, so that the caretaker can be reminded of set up procedures if needed. Step-by-step instructions are also being added to guide the caretaker in the placement of the sensors as this di↵ers across exercises. The system will be tested on another elderly patient using a similar protocol after these improvements. 4.4 Randomized controlled trial A RCT is carried out with the primary goal of evaluating if using the telerehabilitation system in the first three months after the initial discharge results in a greater functional recovery among stroke patients, as compared to usual care. This is a collaboration between NUS, AMK-THKH and SGH (SGH wards in Bright Vision Hospital (BVH)). Within NUS, the medical aspects of the study is managed by Saw Swee Hock School of Public Health (SSH) and the engineering aspects of the study is managed by Department of Electrical and Computer Engineering (ECE). Stroke patients from the participating hospitals are screened against the inclusion and exclusion criteria set out by sta↵ from SSH. If these patients provide an informed consent to participate in the study, they are randomly allocated to one 79 of 2 possible groups: Control and Intervention. The patients in the control group will be advised to attend supervised physiotherapy sessions at a day rehabilitation centre nearby. The patients in the intervention group will be provided with the tele-rehabilitation system at home for the first three months, with the therapist contacting them once a week via FaceTime. The FaceTime sessions will be used to review the patients’ progress for the week and to adjust the difficulty of the exercises if necessary. Both control and intervention group patients undergo an assessment at before the start of the study, at the 3rd month and the 6th month. The assessors performing these assessments will be blinded to whether the patient is in control or intervention group. The results of the assessments will be used to test the hypothesis of the study. Patients in either group can choose to discontinue participation in the study at anytime. The focus of the engineering work of this project is on the intervention group as the control group does not use the tele-rehabilitation system. This section of the chapter will review the data collected from the first three patients and therapists and the usability issues associated with the system. Patient Name Week 1 Week 2 Week 3 A002 SMTWTFS SMTWTFS SMTWTFS B002 SMTWTFS SMTWTFS SMTWTFS B003 SMTWTFS SMTWTFS SMTWTFS A005 SMTWTFS SMTWTFS SMTWTFS Table 4.1: This table provides an overview of days of activity within the first 3 weeks of intervention. Days that are in bold are those days during which the first 4 patients performed exercises. Table 4.4 shows how frequently the first few patients are using the system in the first few weeks of intervention. This is a indication of whether the caretakers and the patients are comfortable with operating the system by themselves. This also indicates if the training with the tele-rehabilitation system that the patients received before their discharge was enough to equip them with confidence and skills to operate the system on their own. It is worth looking at multiple patients as non-compliance to the exercise schedules may not necessarily signal unfamiliarity with the system. The patients are supposed to perform their prescribed exercises 80 everyday. No falls or adverse events were reported for these patients during the weeks shown in Table 4.4. Table 4.4 shows that patient A002’s adherence to the exercise schedule is low, at 2 to 3 days a week. It has been confirmed that A002’s caretaker is able to operate the system and the low adherence was probably due to the other activities that patient A002 is involved in, such as working at a factory, watering plants and feeding birds. A change of caretaker in the third week is also a likely cause of low adherence. There is also another issue that this patient is not very motivated on performing the exercises. After comparing multiple patients’ levels of motivation, it has been determined that this maybe due to the way the pre-discharge hospital training was conducted for A002. For the case of A002, the responsibility of ensuring that the patient regularly performs the exercises was handed to the caretaker. The caretaker was taught how to operate the system with the sensors. For the subsequent patients in the intervention group, this responsibility was assigned to the patient as we taught the patient how to operate the system and wear the sensors. The caretaker’s role would be to help out if necessary. This, together with the low motivation and change of caretaker could account for the patient’s low adherence. 81 Chapter 5 Conclusion 5.1 Conclusion A tele-rehabilitation system has been proposed to improve the adherence of patients to recommended supervised therapy schedule by lowering some of the barriers that prevent them from participating in supervised therapy. The portable system allows a therapist to remotely monitor patients therapy exercises and also prescribe these exercises. The system is being tested in a RCT involving post-discharge stroke patients from AMK-THKH and SGH. While the major software and hardware components of the system are in place, improvements and further developments will be made to the system through the course of the RCT. 5.2 Future Work This chapter will describe the essential and possible future developments or research work that can be pursued on this tele-rehabilitation system. One obvious future work is to finish up the RCT study. Besides the study’s primary medical aim, the study is also instrumental in getting feedback about the system from the users. This feedback allows for changes to be made and tested immediately, as long as it does not a↵ect the fairness of the study. The iteration process of making and 82 testing changes will enable developers to address usability issues. Some of these issues (mostly usability related) and other feedback points with the patient side of the system are listed below: • Clarity of demonstration videos The demonstration videos for exercises involving small limbs such as wrist and ankle do not show the small limb movement clearly. This causes the patient to lean forward and focus on the video before starting to perform the movements on his/her own. • Independent demonstration videos Both patients and therapist seem to be keen on having access to demonstration videos. On the patient side, the videos should be accessible without having to turn the sensors on. This feature is requested with the aim of familiarising the patients with the exercises and familiarising the therapists with the demonstration videos provided to the patients. • Detection of correct position This involves detecting if the patient is indeed in the starting position required for each of the exercises. • Detection of incorrect sensor orientation This involves detecting if the sensor is oriented wrongly on the limb by checking the orientation. This can be used to compensate for the incorrect orientation or to remind the patient of the correct orientation for the limb sensor. • Video upload This is an issue faced by many of the patients and therapists. The patients tend to put their iPads to sleep before the videos of the various exercises get uploaded. This results in the therapist being unable to view the later videos. The issues (mostly usability related) with the therapist side of the system are: • Get therapist application to work on iPad 4 iPad 2 is being phased out by apple and it would be increase the portability of the application if it were 83 to be able to work on retina iPads. Currently this is to possible to due to the incompatibility of the compiled core-plot library. • Alternative method for graph-plotting The graphs are taking a long time to appear as time is required to communicate with the server, process the data and draw the graphs. This process can be sped up through pre-generating the graphs and storing them on the server. The therapist application will just fetch and display the images in this case. • Independent demonstration videos Both patients and therapist seem to be keen on having access to demonstration videos. This feature is requested with the aim of familiarising the patients with the exercises and familiarising the therapists with the demonstration videos provided to the patients. • E-mailing of notes and activity prescriptions Therapists seem to find it useful to have a copy of the call notes and the changes made to the activity prescriptions. • Compliance analytics A summary of how frequently a patient has performed the exercises, together with the number of repetitions performed may be useful to the therapists. Another future work would be to expand the set of exercises o↵ered by the system. These include the integration of the ability to monitor functional movements such as tea-pouring, hair-combing and table-wiping. Lastly, scalability of the system must be considered before allowing public use. The patient and therapist applications must be made available from the Apple’s App Store, in order to aid mass distribution. It would also help in distribution if the applications are ported over to Android. The scalability of the server software can be ensured by moving over to easily scalable cloud services, such as Amazon web services, where the compute capacity and databases can adjust to growing 84 demands. On the hardware side, scalability would involve getting a manufacturer and distributer to make and bring the sensor sets to the customers. The system may also require regulatory approval for use in medical institutions. 85 Appendix A Database Tables Table A.1: Therapist information table (therapist info) to store data identifying the therapists Column Name mySQL field name Variable type Therapist ID therapist id mediumint unsigned not null Username user name varchar(40) Password password blob Facetime e-mail facetime email varchar(100) 86 Table A.2: Patient information table (patient info) to store data identifying the patients Column Name MySQL Field Name 87 Variable Type Remarks Patient ID patient id mediumint unsigned not null Starts from 1, auto-increments SMF trial number trial no varchar(7) Patient initials or name patient initials char(255) Facetime e-mail facetime email Varchar(200) Date of registration dor date ID of therapist assigned to patient. therapist id mediumint unsigned not null Foreign key from table 1. Paralysed side paralysed side varchar(1) L or R iPad number iPad number smallint(6) Site of recruitment (SGH/BVH) site text Whether the initial log is done or not. initial text Whether the 3 month log is done or not three month text Whether the 6 month log is done or not six month text Continued on next page Table A.2 – continued from previous page Column Name MySQL Field Name Date of birth dob date Gender of patient gender text Age of patient age text The ward in which the patient was admit- ward Variable Type Remarks text ted 88 Ethnic group that the patient belongs to ethnic text Marital status of the patient married text Date of stroke onset on the patient dos date Date of admission of the patient to hospi- doa date dod date startdate date tal Date of discharge of patient from hospital, after initial treatment. Date that the patient started the exercise study at home. AKA date of intervention. Continued on next page Table A.2 – continued from previous page Column Name MySQL Field Name Variable Type Date that the patient ended the study. enddate date stroketype text Whether the stroke is recurrent in the pa- strokehist text Remarks This is often 12 to 13 weeks after the startdate. The type of stroke that the patient has tients family or it is recurrent. 89 Modified ranking scale scale text Whether the patient has cardiovascular cardio text Whether the patient has hypertension hyper text Whether the patient has peripheral vascu- vascul text lung text disease lar disease Whether the patient has Chronic Obstructive Lung Disease Continued on next page Table A.2 – continued from previous page Column Name MySQL Field Name Variable Type Whether the patient has Arthritis or Mus- musculo text diabetes text The relationship between patient and caregiver text culoskeletal Condition Whether the patient has Diabetes Mellitus caregiver Remarks 90 Table A.3: Strengthening Activity Lists table to store information about all strengthening activity Column Name Variable type Remarks activity id tinyint unsigned not null Starts from 1, auto-increments activity name varchar(40) upper or lower limb varchar(6) joint ex id tinyint joint id tinyint min angle smallint max angle smallint Table A.4: Strengthening Progression levels table to store information about the progression levels of strengthening exercises Column Name Variable type Remarks progression level ID tinyint unsigned not null Starts from 1, auto-increments progression level name varchar(100) theraband color varchar(10) Null if not applicable Table A.5: Strengthening activity selection table to store the therapists’ strengthening activity prescriptions for the patients. Column Name Variable type Remarks activity ID tinyint unsigned not null Foreign Key from table 3 patient ID mediumint unsigned not Foreign Key from table 2 null Is selected for patient boolean Yes or no, Default: no target angle smallint unsigned Default: null progress level ID tinyint unsigned not null Foreign key from table 4. Default: 1. Cannot be null. remarks text Default: null 91 Table A.6: Strengthening activity record table to store the patients’ strengthening activity repetitions. Column Name Variable type Remarks time of activity timestamp automatically set to row update/ insertion time patient ID mediumint unsigned Foreign Key from tanot null ble 2 activity ID tinyint unsigned not Foreign Key from tanull ble 3 progress level ID tinyint unsigned not Foreign key from tanull ble 4 repetition index tinyint unsigned not null min time int unsigned default value of 0 max time int unsigned default value of 0 minimum angle smallint default -500 maximum angle small int unsigned default -500 file name varchar(50) Null if file storing not successful Table A.7: Compliance form data table to store the number of minutes of exercise per day for every patient Column Name Variable type Remarks trial number smallint unsigned not null foreign key from table 10 day number tinyint unsigned no of mins with therapist smallint unsigned no of mins doing exercise smallint unsigned 92 Table A.8: HR-BP of patients Column Name time of entry patient id therapist id bp systolic bp diastolic heart rate record table to store the heart-rate and blood pressure values Variable type timestamp mediumint unsigned mediumint unsigned tinyint unsigned not tinyint unsigned not tinyint unsigned not Remarks not null not null null null null Table A.9: FaceTime calls record table to by the therapist, and call notes Column Name Variable type button press time datetime not null therapist id mediumint not null patient id mediumint not null end of call time datetime last updated time timestamp was app killed tinyint not null notes by therapist text foreign key form table 2 foreign key form table 1 record the date and time of calls made Remarks foreign key from table 1 foreign key from table 2 Table A.10: iPad record table to keep track of the various iPads that are under use. Column Name Variable type iPad number smallint not null auto increment device serial number varchar(20) null version number varchar(4) null vendor id varchar(40) null Table A.11: Seen graphs record table to keep track of the graph-pages that have been seen by each therapist Column Name Variable type patient id mediumint unsigned not null therapist id mediumint unsigned not null activity id tinyint unsigned not null latest date of exercise in graph date null number of sets on latest date smallint null 93 Appendix B Exercises for testing of sensor accuracy 94 Table B.1: Outline of ROM assessment exercises Upper/ Lower Joint Activity Starting position Activity Name/ROM Angle measured by sensor system Extremity Continued on next page Table B.1 – continued from previous page Upper/ Joint Lower Activity Starting position Activity Angle measured by sen- Name/ROM sor system Extremity Flexion 0 - 180 Shoulder Extension 95 Upper Abduction 0 - 180 The patient can be in The patient moves his/her sitting or standing humerus in an anterior and the humerus and the position. The forearm upward direction. does not need to point The patient moves his/her directly downwards, humerus even though the ROM much as possible. for these activities are calculated from the vertical. posteriorly gravity. as The patient moves his/her humerus laterally and upward as much as possible. Continued on next page This is the angle between Table B.1 – continued from previous page Upper/ Lower Joint Activity Starting position Activity Name/ROM Angle measured by sensor system Extremity Internal The patient can be in The patient is to move This is the angle through 0 - 70 sitting or standing his/her palm towards the which the forearm has position with the elbow abdomen as much as pos- flexed to 90 and the sible while maintaining the initial position and relative forearm in mid-position. 90 flexion of the elbow. 96 Rotation External Rotation The patient is to move 0 - 90 his/her palm away from the abdomen as much as possible while maintaining the 90 flexion of the elbow. Continued on next page moved, starting from the to the initial position. Table B.1 – continued from previous page Upper/ Joint Lower Activity Starting position Activity Angle measured by sen- Name/ROM sor system Extremity Flexion Elbow 0 - 150 97 Pronation 0 - 90 Supination 0 - 90 The patient is in sit- The forearm is moved in an This is the angle through ting or standing position, which anterior direction to bring the forearm has with the elbow extended the palm towards the shoul- moved, starting from the (as close to 0 as possi- der as much as possible. initial position, relative to ble). the upper arm. The patient is in sitting The or standing position about itself so that the which the forearm has been with the elbow flexed to palm faces downwards. rotated. 90 , forearm in mid-position and the palms facing each other. The forearm forearm is is rotated This is the angle through rotated about itself so that the palm faces upwards. Continued on next page Table B.1 – continued from previous page Upper/ Joint Lower Activity Starting position Activity Name/ROM Angle measured by sensor system Extremity Flexion 0 - 80 Wrist The patient is in sitting The patient moves the hand This is the elevation or or standing position in a volar direction as much depression angle through with the forearm resting as possible, with the fingers on the table in relaxed. 98 Extension pronation and the hand 0 - 70 over the end of the table with the fingers relaxed. which the wrist is moved. The patient moves the hand in a dorsal direction as much as possible with the fingers relaxed. Radial Deviation 0 - 20 The patient is in sitting The patient moves the hand This is the heading angle or standing position in the radial direction as through which the wrist with the forearm and much as possible. has been turned. Continued on next page Table B.1 – continued from previous page Upper/ Lower Joint Activity Starting position Activity Name/ROM Angle measured by sensor system Extremity Ulnar Deviation the hand resting on the The patient moves the hand 0 - 30 table in pronation with in the ulnar direction as the fingers relaxed. much as possible. 99 Continued on next page Table B.1 – continued from previous page Upper/ Joint Lower Activity Starting position Activity Name/ROM Angle measured by sensor system Extremity Flexion The patient is to be The hip is flexed to bring This is the angle that the supine with at least the the kneecap as close to the test side (left or right) of chest as possible till the side has swept through, the lower extremity limit of motion. Abduction 0 resting on a plinth. For The hip is abducted to turn its end. - 45 adduction, the hip is to the distal femur of the test 0 - 120 Hip 100 Lower be abducted on the non- side laterally away from the test side at the start. Adduction 0 - 30 non-test side. The hip is adducted to turn the distal femur of the test side laterally towards the abducted non-test side. Continued on next page distal femur on the test from start of the activity to Table B.1 – continued from previous page Upper/ Lower Joint Activity Starting position Activity Name/ROM Angle measured by sensor system Extremity Extension 0 - 30 The patient is to lay side- The hip on the test side is This is the angle that the ways with the hip and extended as much as possi- distal femur on the test side knee of the test side in neutral position. ble. The 101 hip and knee of the nontest side are to be partially flexed to stabilize the pelvis. Continued on next page has swept through. Table B.1 – continued from previous page Upper/ Lower Joint Activity Starting position Activity Name/ROM Angle measured by sensor system Extremity Internal The patient is in sitting The hip is internally rotated This is the angle that is 0 - 45 position with the hip in as much as possible while swept by the tibia of the 90 of flexion and the the position of the femur is test side. knee in neutral rotation, maintained. 102 Rotation External Rotation 0 - 45 with the hip on the The hip is externally ronon-test side abducted tated as much as possible slightly so as to ensure while the position of the fesufficient space for the mur is maintained. external rotation to take place. Continued on next page Table B.1 – continued from previous page Upper/ Joint Lower Activity Starting position Activity Name/ROM Angle measured by sensor system Extremity Knee Flexion 0 - 135 The patient is to be The heel is moved toward This is the angle moved by supine with the test side the buttock as much as pos- the longitudinal axis of the of the lower extremity fibula. sible. resting on a plinth. 103 Flexion Ankle 0 - 20 The patient is to be The ankle is flexed as much This is the angle swept by supine with the ankle as possible while the posi- over the end of the tion of the fibula on the test plinth. side is maintained. Continued on next page the heel. Table B.1 – continued from previous page Upper/ Lower Joint Activity Starting position Activity Name/ROM sor system Extremity Extension 0 - 50 Angle measured by sen- The ankle is extended as much as possible while the position of the fibula on the test side is maintained. 104 Author’s Publications The author has contributed to the following journal publication: Yogaprakash Kumar, Arthur Tay, Wang Wei Lee, Fan Gao, Ziyi Zhao, Jing Ze Li, Benjamin Hon, Tim Tian-Ma Xu, Angela Cheong, Karen Koh, Yee-Sien Ng, Effie Chew, Gerald Choon-Huat Koh, Shih-Cheng Yen, “A wireless wearable rangeof-motion sensor system for upper and lower extremity joints: A validation study in healthy subjects and newly disabled inpatients, Archives of Physical Medicine and Rehabilitation, Under peer review. The author has contributed to the following conferences’ proceedings: 1. A Tay, SC Yen, JZ Li, WW Lee, K Yogaprakash, C Chung, S Liew, B David and WL Au, “Real-time Gait Monitoring for Parkinson Disease, Int. Conf. on Control and Automation., Hangzhou., ICCA 13, 2013, pp. 1796 - 1801. 2. K Yogaprakash and Wee-Seng Soh, “Indoor Location Tracking Using LowCost Modules,” Int. Conf. on Control and Automation., Hangzhou., ICCA ’13, 2013, pp. 1778-1783. 3. K Yogaprakash and Wee-Seng Soh, “Smart Indoor Localisation Technology,” ECE Graduate Student Symposium., Singapore., GSS ’13, 2013, pp. 7. 105 The project has been showcased in the following exhibitions: 1. Infocomm Experience Centre (iExperience) by Infocomm Development Authority of Singapore. June 2012 to May 2013. 2. InnovFest 2014 by NUS Enterprise. 14 to 16 April 2014. 106 Bibliography [1] Neil R. Sims, Hakan Muyderman, “Mitochondria, oxidative metabolism and cell death in stroke”, Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, vol. 1802, Issue 1, pp. 80-91, Jan 2010. [2] World Health Organization, “Global Summary Estimates”, WHO., Geneva, Jun 2013. [3] Karsten Bruins Slot et al., “Impact of functional status at six months on long term survival in patients with ischaemic stroke: prospective cohort studies”, BMJ, vol. 336, no. 7640, pp. 376-379, Feb 2008. [4] Gerald Choon-Huat Koh, Sanjiv K. Saxena, Tze-Pin Ng et al., “E↵ect of Duration, Participation Rate, and Supervision During Community Rehabilitation on Functional Outcomes in the First Poststroke Year in Singapore”, Arch Phys Med Rehabil, vol. 93, pp. 279-286, Feb 2012. [5] Neale R. Chumbler; Patricia Quigley; Xinli Li et al., “E↵ects of Telerehabilitation on Physical Function and Disability for Stroke Patients”, American Heat Association Journals, vol. 43, pp. 2168-2174, May 2012. [6] Brannon JA, Brown CA, Esau TD, et al. “Regulation of telehealth best practices part I”, Federations of State Boards of physical therapy, Denver, 16 Oct 2010. 107 [7] Valerie Hill Hermann, Mandy Herzog, Rachel Jordan et al., “Telerehabilitation and Electrical Stimulation: An Occupation- Based, Client-Centered Stroke Intervention”, American Journal of Occupational Therapy, vol. 64, pp. 73-81, 2010. [8] Center for Assistive Technology and Environmental Access, College of Architecture, Georgia Institute of Technology. (2014). assistivetech.net: National Public Website on Assistive Technology [Online]. Available: http://assistivetech.net/search/productDisplay.php?product˙id=53699 [9] Microsoft. - Microsoft (2014). Store Microsoft - Office Microsoft 365 Store Home [Online]. Software Available: http://www.microsoftstore.com/store/msusa/en˙US/pdp/Kinect-for-Xbox360/productID.253169000 [10] P L Weiss, R Kizony, O Elion, et al. “Development and validation of tele-health system for stroke rehabilitation”, Proc. 9th Intl Conf. Disability, Virtual Reality & Associated Technologies, pp. 33-40, Laval, France, Sept 10-12, 2012. [11] R Kizony, P.L. Weiss, Y. Feldman, et al. “Evaluation of a Tele-Health System for upper extremity stroke rehabilitation”, Intl conf. on Virtual Rehabilitation, pp. 80-86, Philadelphia, Aug 26-29, 2013. [12] Sanchez RJ, Liu J, Rao S, et al. “Automating arm movement training following severe stroke: functional exercises with quantitative feedback in a gravityreduced environment”, IEEE Trans Neural Syst Rehabil Eng, Vol 14, pp. 378389, 2006. [13] David Jack, Rares Boian, Alma Merians et al., “A virtual reality-based exercise program for stroke rehabilitation”, Proc of the fourth international ACM conf on Assistive technologies, pp. 56-63, Nov 13-15, 2000. 108 [14] Beurer. (2013, Apr 4). BM-35 [Online]. Available: http://www.beurer.com/ web/en/suchobjektausgabe.php?pid=5117. [15] Gijbels et al., “The Armeo Spring as training tool to improve upper limb functionality in multiple sclerosis: a pilot study”, Journal of NeuroEngineering and Rehabilitation, Vol 8, pp. 5-11, 2011. [16] S. Madgwick, An efficient orientation filter for inertial and inertial/magnetic sensor arrays, Report x-io and University of Bristol (UK), 2010. [17] Shih-Cheng Yen, Arthur Tay, Yogaprakash Kumar et al., “A wireless wearable range-of-motion sensor system for upper and lower extremity joints: A validation study in newly disabled inpatients”, Archives of Physical Medicine and Rehabilitation, unpublished. [18] Shih-Cheng Yen, Arthur Tay, Yogaprakash Kumar et al., “A wireless wearable range-of-motion sensor system for upper and lower extremity joints: A validation study in healthy subjects”, Archives of Physical Medicine and Rehabilitation, unpublished. [19] Joseph P. Giu↵rida, David E. Riley, Brian N. Maddux et al., “Clinically deployable Kinesia technology for automated tremor assessment”, Movement Disorders, Vol 24, no 5, pp. 723-730, 15 Apr 2009. [20] A Tay, SC Yen, JZ Li et al., “Real-time Gait Monitoring for Parkinson Disease,” Int. Conf. on Control and Automation., pp. 1796-1801, June 12-14, 2013. [21] Mostile G, Giu↵rida JP, Adam OR, et al., “Correlation between Kinesia system assessments and clinical tremor scores in patients with essential tremor”, Movement Disorders, Vol 25, no 12, pp. 1938-1943, 15 Sep 2010. 109 [22] LaStayo PC, Wheeler DL, ”Reliability of passive wrist flexion and extension goniometric measurements: a multicenter study”, Physical Therapy, Vol 74, no 2, pp. 162-74, Feb 1994. 110 [...]... Parkinson Disease,” Int Conf on Control and Automation., pp 1796-1801, June 12- 14, 20 13 [21 ] Mostile G, Giu↵rida JP, Adam OR, et al., “Correlation between Kinesia system assessments and clinical tremor scores in patients with essential tremor”, Movement Disorders, Vol 25 , no 12, pp 1938-1943, 15 Sep 20 10 109 [22 ] LaStayo PC, Wheeler DL, ”Reliability of passive wrist flexion and extension goniometric measurements:... of Singapore June 20 12 to May 20 13 2 InnovFest 20 14 by NUS Enterprise 14 to 16 April 20 14 106 Bibliography [1] Neil R Sims, Hakan Muyderman, “Mitochondria, oxidative metabolism and cell death in stroke”, Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, vol 18 02, Issue 1, pp 80-91, Jan 20 10 [2] World Health Organization, “Global Summary Estimates”, WHO., Geneva, Jun 20 13 [3] Karsten... elevation or or standing position in a volar direction as much depression angle through with the forearm resting as possible, with the fingers on the table in relaxed 98 Extension pronation and the hand 0 - 70 over the end of the table with the fingers relaxed which the wrist is moved The patient moves the hand in a dorsal direction as much as possible with the fingers relaxed Radial Deviation 0 - 20 The patient... Telerehabilitation on Physical Function and Disability for Stroke Patients”, American Heat Association Journals, vol 43, pp 21 68 -21 74, May 20 12 [6] Brannon JA, Brown CA, Esau TD, et al “Regulation of telehealth best practices part I”, Federations of State Boards of physical therapy, Denver, 16 Oct 20 10 107 [7] Valerie Hill Hermann, Mandy Herzog, Rachel Jordan et al., “Telerehabilitation and Electrical Stimulation:... Control and Automation., Hangzhou., ICCA 13, 20 13, pp 1796 - 1801 2 K Yogaprakash and Wee-Seng Soh, “Indoor Location Tracking Using LowCost Modules,” Int Conf on Control and Automation., Hangzhou., ICCA ’13, 20 13, pp 1778-1783 3 K Yogaprakash and Wee-Seng Soh, “Smart Indoor Localisation Technology,” ECE Graduate Student Symposium., Singapore., GSS ’13, 20 13, pp 7 105 The project has been showcased... survival in patients with ischaemic stroke: prospective cohort studies”, BMJ, vol 336, no 7640, pp 376-379, Feb 20 08 [4] Gerald Choon-Huat Koh, Sanjiv K Saxena, Tze-Pin Ng et al., “E↵ect of Duration, Participation Rate, and Supervision During Community Rehabilitation on Functional Outcomes in the First Poststroke Year in Singapore”, Arch Phys Med Rehabil, vol 93, pp 27 9 -28 6, Feb 20 12 [5] Neale R Chumbler;... rehabilitation”, Intl conf on Virtual Rehabilitation, pp 80-86, Philadelphia, Aug 26 -29 , 20 13 [ 12] Sanchez RJ, Liu J, Rao S, et al “Automating arm movement training following severe stroke: functional exercises with quantitative feedback in a gravityreduced environment”, IEEE Trans Neural Syst Rehabil Eng, Vol 14, pp 378389, 20 06 [13] David Jack, Rares Boian, Alma Merians et al., “A virtual reality-based... range-of-motion sensor system for upper and lower extremity joints: A validation study in healthy subjects”, Archives of Physical Medicine and Rehabilitation, unpublished [19] Joseph P Giu↵rida, David E Riley, Brian N Maddux et al., “Clinically deployable Kinesia technology for automated tremor assessment”, Movement Disorders, Vol 24 , no 5, pp 723 -730, 15 Apr 20 09 [20 ] A Tay, SC Yen, JZ Li et al., “Real-time... moves the hand This is the heading angle or standing position in the radial direction as through which the wrist with the forearm and much as possible has been turned Continued on next page Table B.1 – continued from previous page Upper/ Lower Joint Activity Starting position Activity Name/ROM Angle measured by sensor system Extremity Ulnar Deviation the hand resting on the The patient moves the hand 0... Nov 13-15, 20 00 108 [14] Beurer (20 13, Apr 4) BM-35 [Online] Available: http://www.beurer.com/ web/en/suchobjektausgabe.php?pid=5117 [15] Gijbels et al., “The Armeo Spring as training tool to improve upper limb functionality in multiple sclerosis: a pilot study”, Journal of NeuroEngineering and Rehabilitation, Vol 8, pp 5-11, 20 11 [16] S Madgwick, An efficient orientation filter for inertial and inertial/magnetic