- Open Access
Modular mechatronic system for stationary bicycles interfaced with virtual environment for rehabilitation
© Ranky et al.; licensee BioMed Central Ltd. 2014
Received: 1 March 2013
Accepted: 24 April 2014
Published: 5 June 2014
Cycling has been used in the rehabilitation of individuals with both chronic and post-surgical conditions. Among the challenges with implementing bicycling for rehabilitation is the recruitment of both extremities, in particular when one is weaker or less coordinated. Feedback embedded in virtual reality (VR) augmented cycling may serve to address the requirement for efficacious cycling; specifically recruitment of both extremities and exercising at a high intensity.
In this paper a mechatronic rehabilitation bicycling system with an interactive virtual environment, called Virtual Reality Augmented Cycling Kit (VRACK), is presented. Novel hardware components embedded with sensors were implemented on a stationary exercise bicycle to monitor physiological and biomechanical parameters of participants while immersing them in an augmented reality simulation providing the user with visual, auditory and haptic feedback. This modular and adaptable system attaches to commercially-available stationary bicycle systems and interfaces with a personal computer for simulation and data acquisition processes. The complete bicycle system includes: a) handle bars based on hydraulic pressure sensors; b) pedals that monitor pedal kinematics with an inertial measurement unit (IMU) and forces on the pedals while providing vibratory feedback; c) off the shelf electronics to monitor heart rate and d) customized software for rehabilitation. Bench testing for the handle and pedal systems is presented for calibration of the sensors detecting force and angle.
The modular mechatronic kit for exercise bicycles was tested in bench testing and human tests. Bench tests performed on the sensorized handle bars and the instrumented pedals validated the measurement accuracy of these components. Rider tests with the VRACK system focused on the pedal system and successfully monitored kinetic and kinematic parameters of the rider’s lower extremities.
The VRACK system, a virtual reality mechatronic bicycle rehabilitation modular system was designed to convert most bicycles in virtual reality (VR) cycles. Preliminary testing of the augmented reality bicycle system was successful in demonstrating that a modular mechatronic kit can monitor and record kinetic and kinematic parameters of several riders.
Cycling has been used in the rehabilitation of individuals with both chronic conditions such as stroke [1, 2], multiple sclerosis (MS)  and chronic obstructive pulmonary disease  as well as post-surgical populations such as heart , hip  and knee surgery. The proposed and partially documented benefits of cycling are many and include improved aerobic fitness [5, 7], increased muscle strength [1, 8, 9] and even transfer to other activities such as walking [7, 8]. Cycling has been performed in isolation, or in combination with electrical stimulation [1, 7, 8], and augmented with virtual reality .
A classic presentation for individuals with both chronic and post-surgical conditions is lower limb asymmetries in strength, coordination and functional use. These asymmetries have been documented for individuals with MS  and unilateral total hip replacement . Asymmetries have also been identified in stair climbing for individuals with osteoarthritis (OA) of the knee that are asymptomatic . Furthermore, when individuals with motor control asymmetries bicycle for rehabilitation they do so with an asymmetrical pattern. This has been shown for various populations such as individuals with anterior cruciate ligament deficiency  as well as individual post-stroke . These difficulties are in part reversed when cycling is coupled with functional electrical stimulation (FES). However, provision of FES is not always possible. Therefore, among the challenges with implementing bicycling for rehabilitation is the recruitment of both extremities, in particular when one is weaker or less coordinated.
Feedback in the form of virtual reality augmented cycling may serve to address the requirement for efficacious cycling; specifically recruitment of both extremities. Bicycling systems interfaced with virtual reality augmentation are few. They have been used to improve sitting balance and symmetry  and assessed for their psychological benefits to the riders . A bicycling system augmented by virtual reality has not been used however to promote limb symmetry.
Innovations in bicycle hardware have facilitated a more realistic cycling experience by increasing the range of motion of the stationary bicycle or handles [17–22]. Mechanical linkages and dampers allow the handles and bike frame to lean in the coronal and transverse planes to simulate uneven rocky terrain. Developments to address interfacing existing exercise equipment with a computer or electronic device to either translate the rider’s actions as an all-purpose controller or specifically copy their motions into a virtual environment using selected gains have been reported in [23–25]. Heart rate as a surrogate for the rider’s level of exertion has been used in isolation to control the difficulty of a game interfaced with the bicycle . The interfacing of a Virtual Environment (VE) with bicycle however, has not been approached from a multi-modal perspective where physiological and biomechanical measurements are combined and applied to impaired participants.
Instrumented bicycle pedals have been used in evaluating kinetic/kinematic capabilities for subjects with both healthy and plegic lower extremities [27–31]. Experimental setups for pedal force sensing have involved a variety of strain-gauge based designs  and piezoelectric elements [30, 33]. Using an inertial measurement unit (IMU) for detecting pedal angle has not been used before in a clinical setting for stroke rehabilitation. In stationary bicycle pedals the most frequent angle detection methods have been mechanical  or optical-encoder based [20, 27]. An IMU requires no hardware linkage connections which means decreased mechanical complexity and likelihood of component failing.
Adding games to stationary bikes has been used to create several virtual reality cycling systems [17, 18]. These systems were designed for fitness of active individuals, rather than rehabilitation of fitness and motor control deficits of individuals with disabilities. Representative existing systems are prohibitively expensive for a rehabilitation population and provide insufficient feedback to the user. Those systems with proprietary software have the potential to transmit exercise information to the screen and to store information, while others can only drive existing games, controlling only speed or direction. While these systems can perform well for healthy individuals, most of them are too expensive for small clinics and homes.
Although there has been extensive design evolution on bike pedal instrumentation, there has been limited research on incorporating handle bar sensors alongside the pedal sensors for assessing the gripping forces. Furthermore, there are no sensorized exercise bicycle systems that are modular and have the capability of using physiological (heart rate) and biomechanical (kinetics and kinematics) inputs to drive a virtual environment while at the same time collecting performance data. Evaluation of the current commercially comparable devices necessitates a low cost, state of the art system with diverse measurement functionality, immersion, and adaptability to any current stationary bicycle.
In this paper the Virtual Reality Augmented Cycling Kit (VRACK), a virtual reality mechatronic bicycle rehabilitation system is presented. VRACK was designed as a modular system that can convert most bicycles into virtual reality (VR) cycles. Novel hardware components embedded with sensors were implemented on a stationary exercise bicycle to integrate physiological and biomechanical parameters of participants immersed in a virtual environment (VE) providing the user with visual, auditory and haptic feedback. This modular and adaptable system attaches to commercially-available stationary bicycle systems and interfaces with a personal computer for simulation and data acquisition processes. Among the attributes of the VRACK is bike navigation task in which force transducers in the pedals are linked to the verticality of the rider, specifically designed to promote symmetry. In addition heart rate monitoring and feedback are used to promote exercise intensity suitable for health and fitness.
In the virtual environment, a pace rider is displayed as a visual target to motivate the patient. The patient is instructed to catch the pace rider, who rides at the patient’s target heart rate (HR). Previous systems have not used this dynamic velocity tool for HR and used either fixed HR to induce a level of patient exertion or HR scaling to level the playing field between human players of different fitness level . The RE07L Wireless Receiver Module and T31 coded elastic chestband (Polar Electro Inc., Lake Success, NY, USA) was selected for HR measurement. The chest-band is worn during exercise with the transmitter in skin contact just below the center of the sternum, and outputs a pulse for each heart beat.
Instrumented handlebar module
The surfaces contacting the tubing are designed after a simply supported beam where the sum of the support reaction forces equals the loads from the hand as the tubing compression forces. This tubing configuration is designed to keep the paddle contacting the hand self-balancing since it is supported by two sections of the same chamber. This way a force applied at any location between the tube sections is distributed evenly across the entire chamber and transmitted to the hydraulic pressure sensor (Model PX 35, Omega Engineering). The liquid inside the tube is a medium-density mineral oil which is non-reactive and stable for ranges of room temperature.
Instrumented pedal module
The forces on the plate are measured by a single-axis low profile compression load cell (LC302-500, Omega, Stamford, CT, USA). Four bolt & spring assemblies provide a collective 50 lb (222.4 N) pre-load compression on the pedal system to enable the single-axis load cell to detect tensile forces in the pedal. The resulting offset for the load cell voltage is zeroed in software. This enables measurement of tensile force up during pedalling to this pre-load max, and compressive forces up to 450 lbs (2001.7 N).
The pedal tilt of the ankle is monitored by an inertial measurement unit (IMU), which contains an accelerometer and gyroscope to detect tilt in dorsi and plantarflexion. The raw data from the accelerometers and gyroscope are collected from the practitioner interface and then analysed using a Kalman Filter [39, 40]. The rate gyroscopes are used to determine the angular velocity (ω) of the crank corrected each revolution by the infrared interrupters. Infrared reflectors (IR) were implemented to control the drift of the IMU as well as measure the number of rounds per minute (RPM) of the crank. Every time the crank arm passes in front of the pedal-mounted sensor, this indicates that the pedal is perpendicular to the crank which is used as a reference to zero the drift from the IMU. At the same time, two small IR sensors are mounted on the body of the stationary bike, one to face each pedal. These body-mounted IR posts detect the number of times the crank passes the top-dead-center position and thus calculate the RPM.
To provide haptic feedback to the rider’s feet, vibration elements (Precision Microdrives 310–101, Precision Microdrives, London, UK) were implemented in the pedal bindings. Two of these elements were encased and attached to the inside of the bindings with Velcro so can be relocated anywhere on the dorsal shoe surface. They are activated manually to augment sensory input to the foot.
Data acquisition & user interfaces
Each handlebar has a single signal displayed on the PI for the magnitude of the net force, but the near and far surfaces of each handle are being recorded separately and summed for the net force. The sign of this magnitude is the direction indicating towards or away from the rider. The filler bar is used to adjust the gain of the handlebars impact in the VR simulation. The scaling operation takes place after being displayed in the PI and only affects the VE.
Each pedal has its output displayed as a single net force, with the sign indicating compression or tension. Patients with lower extremity strength or coordination asymmetries cannot isolate the performance of the unaffected side compensating for the affected side. Since current stationary bikes (and also higher-end systems) record output power measurements from the flywheel at the middle of the two bonded crank arms this convolutes the output. Separating pedal force sensing components for each foot is the only way to remove this convolution effect of the unaffected side compensating for the affected side. The pedal angle is displayed using two gauges that span from 90° to -90°.
Each heart beat illuminates a light on the PI and heart rate (beats per minute) is displayed. The vibration mode can be toggled between automatic and manual (intensity – frequency controlled by the practitioner individually for each foot). Locating the vibration elements on the surface of the rider’s feet allows testing for sensation of the affected vs. unaffected sides.
Virtual environment & sensor mapping
Within the simulation the handlebars control the trajectory of the virtual rider and differential forces from the left and right handles are subtracted after being acquired in the practitioner’s interface. The final net force steers the virtual bicycle through the simulation. The net force from the two pairs of surfaces will be either a clock-wise or counter clock-wise moment with respect to the position of the front fork of the bicycle. Each of the four sensing surfaces is applied a sign based on the moment it generates. The four individual values are recorded as well as the net from each handle.
For steering within the virtual environment it is important to mimic the reactions from a real bicycle closely to promote user immersion. However, even for a consistent smooth turn there are some oscillations in the handle trajectory, which could be visually disturbing if not set correctly in the simulation visuals. This necessitates an artificial dead zone in the software for the handles to avoid sudden and erratic motions of the virtual rider. Even straight, level pedalling regular motion causes slight oscillations in the upper trunk and handlebar trajectory. The pedalling movement transferred to the arms from the legs has been shown to induce roughly a 2.5° periodic sway even in healthy riders for properly fitted handlebars on a track bicycle . This must be accounted for to determine the dead zone implemented in the software so that it does not affect the data collection, only the visual feedback of the simulation. However this does not have to include the counter-steering effect (occurring during controlled turning of a real bicycle or motorcycle) because the torque experienced prior to the turn is already negligible .
The modular mechatronic kit for exercise bicycles was tested in bench testing and rider tests. Results from these tests are described in this section.
Sensorized handlebar dynamic testing
Pedal static force tests
IMU Calibration and validation using a specially design testbed
Characterization of the VRACK instrumentation beyond bench testing is necessary to validate both the hardware and the software with individuals riding the bicycle. In this paper we present data obtained from the instrumented pedals when they were manually moved and with individuals riding the bicycle. In a recent study we have also validated VE features such as optic flow effects on riders cycling performance .
The focus of the rider experiments presented in this section was to validate the data (i.e. pedal angle and pedal forces) obtained using the left and right instrumented pedals during a cycling session. During these experiments we concurrently collected kinematic data with both the VRACK IMU and a Peak Motus motion capture system and compared them. Kinetic data were collected using the pedal force sensors and compared with similar data reported in literature.A six camera Peak Motus motion capture system was used to record the kinematics at 60 Hz during pedaling on a recumbent bicycle (Biodex, SRC). Simultaneously the VRACK system collected the data from the bicycle’s pedal IMU at 100 Hz using a real-time Labview program as described in Figure 4. Data from the Peak were re-sampled to 100Hz to match the IMU sampling frequency. Data were synchronized by matching the peak pedal angle with the first five seconds of the trial. The pedal marker data from the Peak Motus system were used to measure the pedal orientation and cycling RPM. The marker data were processed and gaps were filled using smoothing spline function post collection. The orientation of the line joining the marker in the front and back of the pedal was calculated to obtain the pedal angle. The data from the VRACK system were analyzed to extract the pedal orientation and forces.
Experiment 1 - hand driven pedal motions
Experiment 2 - riders biking in the VE
Five healthy participants (18–35 years) without any mobility, functional, or cardiovascular disorders provided their informed consent and participated in the study. The study was approved by the Institutional Review Board at the UMDNJ.
Following an orientation to the protocol, subjects were seated on the bike and positioned with 50 degrees of knee flexion when the pedal was at bottom dead center and parallel to the ground. The power on the bike was set to a constant 20 watts. Subjects warmed up by pedaling at a comfortable speed for 3 minutes. They were instructed to pedal at their slow and comfortable speeds, keeping both hands on the handle-bars, and looking in front of them. Pedals were instrumented with three reflective pedal markers; one each on the center, back, and front lateral edge of each pedal. Data were collected for three trials of thirty to forty-five seconds each.
In this paper the virtual reality augmented cycling kit (VRACK), a mechatronic rehabilitation system with an interactive virtual environment, was presented. VRACK consists of sensorized pedals, handlebars and a heart rate monitor interfaced with a virtual biking environment. VRACK was designed to benefit users with riding asymmetry by using quantitative measures to dynamically direct their attention. Work with individuals post-stroke who are present with fitness deficits and riding asymmetry is underway and the preliminary findings are encouraging .
VRACK and its modules offer several possibilities to augment existing home-based exercise equipment or used separately as stand-alone modules depending on what exercise is prescribed. The hydraulic chamber design of the handles could also be separated into smaller arrays of sensing regions to monitor more surfaces across the hands for either healthy or impaired individuals. For low ranges of upper extremity loading it could function as a computer interface/virtual reality device or dexterity training tool. For medium ranges of loading the design can be modified to map force distribution for power grasping pull tasks like lifting a briefcase or moving objects.
The VRACK system includes signals from 15 different sensors. This large number of sensors necessitates the need for robust signal acquisition hardware and software with proper filtering. Making the handle and pedal modules wireless will ease installation and reduce potential tripping hazard from the tethered modules. This will also be pragmatic for groups of VRACK systems to operate side by side for group exercise sessions in a clinical setting. Ultimately the VRACK’s relevance will be established when riders can modify their cycling kinetics from asymmetrical to symmetrical patterns, improve their fitness and more importantly transfer the benefits from training in the VE to real world mobility.
This work was supported by the National Institutes of Health (NIH) with the grant R41 HD54261 (PI Judith Deutsch). The authors would like to thank Mr. Alan Argodizza for participating in the development of the testbed for IMU calibration and validation.
- Ambrosini E, Ferrante S, Pedrocchi A, Ferrigno G, Molteni F: Cycling induced by electrical stimulation improves motor recovery in post acute hemiparetic patients: a randomized controlled trial. Stroke 2011,42(4):1068-1073. 10.1161/STROKEAHA.110.599068View ArticlePubMedGoogle Scholar
- Katz-Leurer M, Sender I, Keren O, Dvir Z: The influence of early cycling training on balance in stroke patients at the subacute stage. Results of a preliminary trial. Clin Rehabil 2006,20(5):398-405. 10.1191/0269215505cr960oaView ArticlePubMedGoogle Scholar
- Mostert S, Kesselring J: Effects of a short-term exercise training program on aerobic fitness, fatigue, health perception and activity level of subjects with multiple sclerosis. Mult Scler 2002,8(2):161-169. 10.1191/1352458502ms779oaView ArticlePubMedGoogle Scholar
- Pitta F, Brunetto AF, Padovani CR, Godoy I: Effects of isolated cycle ergometer training on patients with moderate-to-severe chronic obstructive pulmonary disease. Respiration 2004,71(5):477-483. 10.1159/000080632View ArticlePubMedGoogle Scholar
- Eder B, Hofmann P, Von Duvillard SP, Brandt D, Schmid JP, Pokan R, Wonisch M: Early 4-week cardiac rehabilitation exercise training in elderly patients after heart surgery. J Cardiopulm Rehabil Prev 2010,30(2):85-92. 10.1097/HCR.0b013e3181be7e32View ArticlePubMedGoogle Scholar
- Cukras Z, Praczko K, Kostka T, Jegier A: Physical activity of elderly patients after total hip arthroplasty. Ortop Traumatol Rehabil 2007,9(3):286-296.PubMedGoogle Scholar
- Janssen TW, Beltman JM, Elich P, Koppe PA, Konijnenbelt H, De Haan A, Gerrits KH: Effects of electric stimulation-assisted cycling training in people with chronic stroke. Arch Phys Med Rehabil 2008,89(3):463-469. 10.1016/j.apmr.2007.09.028View ArticlePubMedGoogle Scholar
- Lee MJ, Kilbreath SL, Singh MF, Zeman B, Lord SR, Raymond J, Davis GM: Comparison of effect of aerobic cycle training and progressive resistance training on walking ability after stroke: a randomized sham exercise-controlled study. J Am Geriatr Soc 2008,56(6):976-985. 10.1111/j.1532-5415.2008.01707.xView ArticlePubMedGoogle Scholar
- Ferrante S, Ambrosini E, Ravelli P, Guanziroli E, Molteni F, Ferrigno G, Pedrocchi A: A biofeedback cycling training to improve locomotion: a case series study based on gait pattern classification of 153 chronic stroke patients. J Neuroeng Rehabil 2011, 8: 47. 10.1186/1743-0003-8-47View ArticlePubMedPubMed CentralGoogle Scholar
- Chen CH, Jeng MC, Fung CP, Doong JL, Chuang TY: Psychological benefits of virtual reality for patients in rehabilitation therapy. J Sport Rehabil 2009,18(2):258-268.PubMedGoogle Scholar
- Chung LH, Remelius JG, Van Emmerik RE, Kent-Braun JA: Leg power asymmetry and postural control in women with multiple sclerosis. Med Sci Sports Exerc 2008,40(10):1717-1724. 10.1249/MSS.0b013e31817e32a3View ArticlePubMedGoogle Scholar
- Talis VL, Grishin AA, Solopova IA, Oskanyan TL, Belenky VE, Ivanenko YP: Asymmetric leg loading during sit-to-stand, walking and quiet standing in patients after unilateral total hip replacement surgery. Clin Biomech (Bristol, Avon) 2008,23(4):424-433. 10.1016/j.clinbiomech.2007.11.010View ArticleGoogle Scholar
- Liikavainio T, Isolehto J, Helminen HJ, Perttunen J, Lepola V, Kiviranta I, Arokoski JP, Komi PV: Loading and gait symmetry during level and stair walking in asymptomatic subjects with knee osteoarthritis: importance of quadriceps femoris in reducing impact force during heel strike? Knee 2007,14(3):231-238. 10.1016/j.knee.2007.03.001View ArticlePubMedGoogle Scholar
- Hunt MA, Sanderson DJ, Moffet H, Inglis JT: Interlimb asymmetry in persons with and without an anterior cruciate ligament deficiency during stationary cycling. Arch Phys Med Rehabil 2004,85(9):1475-1478. 10.1016/j.apmr.2003.10.017View ArticlePubMedGoogle Scholar
- Sibley KM, Tang A, Brooks D, Brown DA, McIlroy WE: Feasibility of adapted aerobic cycle ergometry tasksto encourage paretic limb use after stroke: A case series. J Neurol Phys Ther 2008,32(2):80-87. 10.1097/NPT.0b013e318176b466View ArticlePubMedGoogle Scholar
- Song CG, Kim JY, Kim NG: A new postural balance control system for rehabilitation training based on virtual cycling. IEEE Trans Inf Technol Biomed 2004,8(2):200-2007. 10.1109/TITB.2004.828887View ArticlePubMedGoogle Scholar
- The Expresso Family of Interactive Bikes by Interactive Fitness Holdings. . Accessed January 14, 2012 http://www.expresso.com
- Dogfight Pedal Game Flight Simulator by Electronic Sports. . Accessed January 14, 2012 http://electronicsports.com
- Ware JS: Variable Pitch Stationary Exercise Bicycle. US Patent; 2003. 6, 648,802Google Scholar
- Smith DB, Miller RF, Moore JM: Pedal-Operated Stationary Exercise Device. US Patent; 1986. 4,592,544Google Scholar
- Schenk P, Clem WE, Williamson C, Albini M, Eghbal D, Green EG: Stationary Exercise Bicycle With Shock Absorption System. US Patent; 2003. US 6,508,745Google Scholar
- Nusbaum NH: Exercise Bicycle Virtual Reality Steering Apparatus. US Patent; 2005. 6,918,860Google Scholar
- Haydocy CJ, Eberhart HD, Caruso RJ: Exercising Apparatus which Interacts with A Video Game Apparatus during Exercise. US Patent; 1997. 5,645,513Google Scholar
- Virkkala AJ: Apparatus for Connecting An Exercise Bicycle to A Computer. Finland Patent; 1998. 5839990Google Scholar
- Parks EH: Apparatus for Removably Interfacing A Bicycle to A Computer. US Patent; 2003. 6,530,864Google Scholar
- Hall-Tipping H: Video Game Difficulty Level Adjuster Dependent Upon Player’s Aerobic Activity Level During Exercise. US Patent; 1991. 5,001,632Google Scholar
- Brown DA, Kautz SA: Increased workload enhances force output during pedaling exercise in persons with poststroke hemiplegia. Stroke 1998, 29: 598-606. 10.1161/01.STR.29.3.598View ArticlePubMedGoogle Scholar
- Brown DA, Kautz SA, Dairaghi CA: Muscle activity adapts to anti-gravity posture during pedalling in persons with post-stroke hemiplegia. Brain 1997, 120: 825-837. 10.1093/brain/120.5.825View ArticlePubMedGoogle Scholar
- Burke ER: High-Tech Cycling. 2nd edition. Colorado Springs: Human Kinetics; 2003.Google Scholar
- Hoes MJAJM, Binkhorst RA, Smeekes-Kuyl AEMC, Vissers ACA: Measurement of forces exerted on pedal and crank during work on a bicycle ergometer at different loads. Eur J Appl Physiol 1968,26(1):33-42. 10.1007/BF00696088View ArticleGoogle Scholar
- Rogers LM, Brown DA, Gruben KG: Foot force direction control during leg pushes against fixed and moving pedals in persons post-stroke. Gait Posture 2003,19(1):58-68.View ArticleGoogle Scholar
- Sargeant AJ, Hoinville E, Young A: Maximum leg force and power output during short-term dynamic exercise. J Appl Physiol Respir Environ Exerc Physiol 1981,51(5):1175-1182.PubMedGoogle Scholar
- Sanderson DJ, Hennig EM, Black AH: The influence of cadence and power output on force application and in-shoe pressure distribution during cycling by competitive and recreational cyclists. J Sports Sci 2000,18(3):173-181. 10.1080/026404100365072View ArticlePubMedGoogle Scholar
- Rowe T, Hull ML, Wang EL: A pedal dynamometer for off-road bicycling. J Biomech Eng 1998,120(1):160-164. 10.1115/1.2834297View ArticlePubMedGoogle Scholar
- Stach T, Graham TC, Yim J, Rhodes RE: Heart Rate Control of Exercise Video Games. In Proceedings of Graphics Interface 25-29 May. Kelowna, Canada; 2009:25-27.Google Scholar
- Ayoub MM, Presti PL: The determination of an optimum size cylindrical handle by use of electromyography. Ergonomics 1971,14(4):509-518. 10.1080/00140137108931271View ArticlePubMedGoogle Scholar
- Drury CG: Handles for manual materials handling. Appl Ergon 1980,11(1):35-42. 10.1016/0003-6870(80)90120-9View ArticlePubMedGoogle Scholar
- Kong YK, Lowe BD: Optimal cylindrical handle diameter for grip force tasks. Int J Ind Ergon 2004,35(6):495-507.View ArticleGoogle Scholar
- Kalman RE: A new approach to linear filtering and prediction problems. Trans ASME J Basic Eng 1960,82(Series D):35-45.View ArticleGoogle Scholar
- Luinge HJ, Veltink PH: Measuring orientation of human body segments using miniature gyroscopes and accelerometers. Med Biol Eng Comput 2005,43(2):273-282. 10.1007/BF02345966View ArticlePubMedGoogle Scholar
- Fajans J: Steering in bicycles and motorcycles. Am J Phys 2000,68(7):654-659. 10.1119/1.19504View ArticleGoogle Scholar
- Gade V, Maidan I, Gallagher R, Torres C, Deutsch JE: Cycling Rate is Modulated by Optic Flow in a Virtual Bicycle Environment. In Proceedings of the IEEE 2011 . Zurich Switzerland: International Conference on Virtual Rehabilitation (ICVR); 2011:27-29.Google Scholar
- Brown DA, Kautz SA: Speed-dependent reductions of force output in people with poststroke hemiparesis. Phys Ther 1999,79(10):919-930.PubMedGoogle Scholar
- Johnston TE, Prosser LA, Lee SC: Differences in pedal forces during recumbent cycling in adolescents with and without cerebral palsy. Clin Biomech (Bristol, Avon) 2008,23(2):248-251. 10.1016/j.clinbiomech.2007.08.028View ArticleGoogle Scholar
- Deutsch JE, Myslinksi MJ, Kafri M, Ranky R, Sivak M, Mavroidis C, Lewis JA: Feasibility of virtual reality augmented cycling for health promotion of people poststroke. J Neurol Phys Ther 2012,37(3):118-124.View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.