Faisal,

Lin, C

English

Falls are the leading causes of unintentional injuries
in the elderly and thus a pose a major hazard to
our ageing society. We present the FOHEPO (FOot HEight
POsitioning) system to measure, diagnose and rehabilitate
ageing-related and neurological causes of falls. We hypothesise
that both perceptual and motor variability is likely to increase
with age and may lead to imprecise movements causing trip
overs, the major triggers of falls. Here we propose a robotic
system that automatically performs measures of fall-related
perceptual and motor variability in elderly subjects. Our
FOHEPO platform enables us to measure and track different
sources of noise in the nervous system: visual perception noise of
obstacle height, proprioceptive noise of localising raising one’s
foot to a desired height, noise in the visual feedback of the
foot movements. The platform should eventually provide us the
means to estimate fall probabilities by using these measures.
Crucially, we can use the same system in game-field settings to
rehabilitate elderly users moving with larger safety factors so
as to reduce their risks of trip-over

Faisal, AA

Spiral - Imperial College Digital Repository

Robotic psychophysics rig to assess, diagnose and rehabilitate theneurological causes of falls in the elderlyChin-Hsuan Lin1,MD and A. Aldo Faisal1,2,3, Member IEEEAbstract— Falls are the leading causes of unintentional in-juries in the elderly and thus a pose a major hazard toour ageing society. We present the FOHEPO (FOot HEightPOsitioning) system to measure, diagnose and rehabilitateageing-related and neurological causes of falls. We hypothesisethat both perceptual and motor variability is likely to increasewith age and may lead to imprecise movements causing tripovers, the major triggers of falls. Here we propose a roboticsystem that automatically performs measures of fall-relatedperceptual and motor variability in elderly subjects. OurFOHEPO platform enables us to measure and track differentsources of noise in the nervous system: visual perception noise ofobstacle height, proprioceptive noise of localising raising one’sfoot to a desired height, noise in the visual feedback of thefoot movements. The platform should eventually provide us themeans to estimate fall probabilities by using these measures.Crucially, we can use the same system in game-field settings torehabilitate elderly users moving with larger safety factors soas to reduce their risks of trip-over.I. INTRODUCTIONMany fundamental health challenges faced by modernsocieties are caused by rapid population ageing. Fallingconstitutes one such challenge and is the leading causeof unintentional injuries in the elderly [1]. Falls result ininjuries [2], reduced life quality [3] and decreased survival[4]. Comprehensive studies using symptomatic descriptionsor biomechanical measurements have made significant pro-gresses in the understanding of falls in the elderly [5], [6],[7]. However, less has been done to understand falls from acomputational and motor neuroscience way. Computationalprinciples in motor control enable us to connect ageing-related brain pathology to changes in biomechanical andphysiological markers – and in the end to falls themselves.One key aspect is obstacle clearance, the minimum dis-tance between the inferior surface of foot/shoe and theobstacle when stepping over it: Clipping the obstacle dueto negative clearance will result in a trip event – which arethe major main cause of falls in the elderly [8]. However,the mechanisms causing a decrease in the performanceof obstacle clearance in fall-prone elderly people remainunexplored. Both active obstacle clearance (consciously in-creasing obstacle clearance) and an increase in obstacleclearance variability [6] were found to link to increasedrisks of falling. The former variability we associate withsensory/perceptual noise about the height of the obstacle, theBrain & Behaviour Lab - 1Dept. of Bioengineering & 2Dept. of Com-puting, Imperial College London, South Kensington Campus, SW7 2AZ,London, UK,3Imperial Clinical Research Facility , Imperial Centre forTranslational and Experimental Medicine, W12 0NN London, UK - a.faisalat imperial.ac.ukletter variability we associate with variability in movementtrajectories, contributed by motor noise. We propose that age-related changes of sensory and motor uncertainty, as well asthe sensorimotor computations that mitigate uncertainty (cueintegration and motor coordination), cause the alteration ofobstacle clearance in potential elderly fallers. To tackle thesequestions we require controlled experiments that capturethe sensorimotor loop (perception, action, environment) andtherefore requires the targeted development of experimentalset-up in which to study these.Here we designed a controlled robotic psychophysicaltesting environment to present subjects with naturalistic, yethighly controlled stimuli, that enable us the estimation ofsensorimotor noise and integration in lower extremities. Also,using this paradigm, we tested our hypothesis of increasednoise level as a cause of increased obstacle clearance vari-ability in aged people. We devised foot height positioningtask (FOHEPO), a task adapting clearing an obstacle, tomeasure sensorimotor variability. Unlike real obstacle clear-ing. Our foot height positioning task does not endangersubjects being in contact with an obstacle and thereforehas minimal risk of falling, yet it mimics the scientificallyrelevant elements of obstacle clearing.II. METHODSA. Design of the FOHEPO rigThe FOHEPO (FOot HEight POsitioning) rig (Fig 1.)involves a robotic automated platform, hand rails to supportsubjects, as well as motion tracking devices and a computersystem. Our system allows a close-loop interaction of humansubjects and a computer-controlled experimental paradigm.The general experimental set-up consists of an one sideopen metal rig (length 1180mm × width 800mm × height1040 mm, open side: 800 mm) with bilateral handrails. Ourautomated movable target is 900mm away from a subject.This suffices the usual distance when an observer has toclear o obstcle avoidance during ambulation . Four Firgelli(Firgelli, Victoria(BC/Canada) linear actuators (Firgelli L16-140-63-12-S) are operated synchronously via Arduino Unomicro-controllers to move the object between trials.The ismade of a white polystyrene sheet length 760 mm x width300 mm x thickness 5 mm. Since the floor and walls ofthe rig are covered with black floor mats and cardboards,there is a sufficient visual contrast between the target and itssurroundings (Fig. 1. C).During the experiment, the positions of subjects’ movingfoot and the target as well as their relative height differencesare constantly monitored by three motion capture camerasFig. 1. Experimental setup: (A) displays the system’s components which interact during an actual experimental session and also provide online feedbackof the monitored performance measured by the motion capture system (B) The object position is controlled by a PC-run software module, through ArduinoUno micro-controllers. The button sending signals to the PC also connects via the mirco-controller (C) The setup in the laboratoryat 120 Hz (Optitrack Flex 13; NaturalPoint, Inc. Corval-lis(OR/U.S.A.)). Our system were tested by using objectswith known heights of 90 mm, and was proved to provideexcellent precision and stability. The mean error is 0.5 mmand the drift over 1 hour is less than 0.05 mm.B. Experimental Protocol for the Foot Height PositioningTask (FOHEPO Task)In all trials, subjects are required to lift one of their feetto match the height of the shoe tip marker and the target.They are asked to keep their feet level. However, in orderto keep a natural body positioning and allow the subjectsto use visual information of their foot positions, movementsalong the sagittal plane of their bodies are suggested. Beforethe beginning of each trial, actuators move the target to apre-set height. Three consecutive beeps of 0.1 sec durationand 0.85 sec inter-beep interval are played for 2 sec once theobject reaches its target level. The first two beeps are servedas warning signals for subjects to get prepared. The last beepis the signal to move. Subjects are asked to reach the targetlevel as fast and accurate as possible.They are instructed topress a button on the left handrail when they consider theirfoot matches the target height. After pressing the button theyhave to keep their foot still for 1 sec before hearing anotheraudio signal, which indicates the end of this trial and alsoprovides performance feedback (positive when the differencebetween the foot and the object less than 35 mm; otherwisenegative).The start of a trial is defined as the moment whenthe third beep begins. If a subject does not press the buttonwithin 4 seconds after a trial starts, this trial is terminatedautomatically and labelled as a ”mis-trial”. Subjects redo allmis-trials at the end of every block. If a subject still failsto complete a trial within 4 second in one’s second try, thattrial is excluded from data analysis.Three height conditions, 50, 100, 150 mm, are used toprevent stereotypical behaviours and self repeating. Also, thisheight range covers usual human step height both in walkingand in stair-climbing. Heights are chosen in pseudorandomorders. There are 40 trials per condition, 20 for each foot.Trials are separated into 4 blocks. Alternative foot order areused (either R-L-R-L or L-R-L-R).For each trial, the average of all foot height data acquiredduring the 1 second after button press is taken as the footheight. ’Movement time’ is calculated by subtracting thebeginning time of the final beep from the time button pressed.The primary outcome are the mean variance and standarddeviation of foot height for each condition and each foot, asthe quantification off sensorimotor noise, and the mean offoot height for each condition each foot as the precision offoot placement.C. Psychophysics technique to measure sensory noiseTo better understand how sensory noise can be estimatedby psychophysics, we apply a psychometric function inFig. 2. Foot height trajectories for height matching task (lift up of foot from 0 to 50, 100, 150mm target height) for left and right foot. A Young subjectB Elderly pilot subjects. trajectory over time in A.a healthy young pilot B. a healthy elderly pilot. The red crosses are the time when the rig’s button waspressed to signal task completion.Fig. 3. A Simulated results of median estimated sigma using 4 different combinations of trial number (N), number of stimulus level (k) and stimulusrange (Range, in Z unit). Simulation methods were adapted from Simulatory observers’ lapse rate lambda values were set to be one of 0, 0.01, 0.02, 0.03,0.04, 0.05. B Using parameters which generated best-fitted reaults in A (N=120, k=4, Range= 3Z), object 2AFC was performed with a young volunteer.The results and fitted psychometric function are presented.the context of signal detection theory (SDT) [9]. SDTassumes that the strength of each sensory input is internallyrepresented as independent Gaussian variables. In 2AFCdiscrimination tasks, SDT can be comprehended by a simplerdifference distribution of internal sensory responses. On eachtrial, observers compare the difference between two internalresponses and pick the stimulus based on the sign of thedifference. We assigned standard stimulus intensity to bes and comparison stimulus with varying intensity acrosstrials to be c. If the probability density functions of theinternal representation for s and c are Gaussian with identicalvariance σ2. The psychometric function is the Gaussian cu-mulative density function of observed difference distributionwith variance of 2σ2. Therefore, acquiring a psychometricfunction experimentally provides an estimate for the noiseof the underlying distribution.Object height and foot height 2AFC tasks are used tocharacterise visual noise of object height estimation andproprioceptive noise of foot height estimation. In all sensorytasks, subjects are standing in the robotic experiment envi-ronment the same as the one in FOHEPO. In object 2AFC,subjects choose the higher interval in each trial. In foot height2AFC, Subjects are asked to choose if the passively movedfoot or the object is higher. Here, one of their feet will belifted and supported by a small-sized platform. Therefore,motor noise is minimised, if not eliminated, while sensorynoise is retained. Experiment is divided into two phaseswhere 1. the moving foot is visible 2. the moving foot isoccluded. Both phases enable subjects to quantify body(footin this experiment) position sensory variability when subjectsreceive both visual and proprioceptive information (firstphase) and when they only have proprioceptive information(second phase).III. RESULTSThe FOHEPO system was tested with a healthy youngand a healthy elderly subject. Both subjects completed theFOHEPO tasks within 40 minutes. As can be seen from themovement trajectories (see Fig 2.A,B), the young subjecttypically had shorter movement time, pressed the buttonearlier and thus completed trials faster then the elderlysubject. No significant difference between feet were notedin the means and standard deviations of foot height andmovement time. Therefore, data from both feet were pooledtogether for further analysis.The elderly volunteer showed a higher degree of footplacement variability comparing to the young counter part(standard deviation at 50-100-150 mm: young: 10.2 mm-8.7 mm-15.2 mm; elderly: 18.4 mm-27.0 mm-22.1 mm).This finding is in agreement with our hypothesis. Interest-ingly, the elderly subject tended to place his foot higher(mean 101.7 mm-130.2 mm-175.2 mm) than the youngercounterpart (52.5 mm-93.0 mm-128.9 mm) and the realobject height, suggesting perhaps an age-dependent increasein safety factors for foot clearance.To obtain an accurate estimate of σ2, more than 240 trialsare suggested by literature [10]. However, our trial number islimited by the characteristic of our study population, elderlypeople, and instruments, a linear motor controlled movingobject. Computing simulation and pilot experiments wereused to determine optimal trial number, condition numberand stimulus range for our psychophysics experiment. Wefound a design with N = 120 trials, number of conditions =4 and stimulus spacing of 18 mm providing us a satisfyingresult for object 2AFC(Fig 3.A,B). Our young subject com-pleted the experiment in 45 mins. Using the same design,trial numbers in foot 2AFC can be doubled to 240 withoutincreasing total experiment time since there is only 1 intervalfor each trial.IV. DISCUSSIONWe designed a closed-loop robotic environment for theresearch of sensorimotor uncertainty and control in lowerextremities. Our FOHEPO provides an undemanding methodto understand the relationship between sensorimotor uncer-tainty and fall risk in elderly people systematically. Weimplemented the FOHEPO in one young and one elderlyvolunteer. Preliminary results were compatible with ourassumptions. Larger foot placement variability, higher footplacement, and longer movement time were observed. Moresubjects will be included in order to prove the hypothesis.Individual elements of sensory noise will be extracted usingpsychophysics. By doing so, the contribution of each elementto ageing-related changes in sensorimotor uncertainty can beclarified.Potentially, the methods can be applied to high risk elderlygroups and patients with fall-prone neurological disorders,such as Parkinson’s disease and Alzheimer’s disease, toeluciate the underlying mechanisms causing falls in theseconditions. Moreover, the robotic environment can be usedas an assisting rehabilitation tool for fall prevention in highrisk groups.ACKNOWLEDGMENTS*This work was supported by fellowship of the TaiwaneseMinistry of Education (1001163-2-UK-003) to CHL.REFERENCES[1] L. Z. Rubenstein, “Falls in older people: epidemiology, risk factorsand strategies for prevention,” Age and ageing, vol. 35, no. suppl 2,pp. ii37–ii41, 2006.[2] P. Kannus, H. Sievänen, M. Palvanen, T. Järvinen, and J. Parkkari,“Prevention of falls and consequent injuries in elderly people,” TheLancet, vol. 366, no. 9500, pp. 1885–1893, 2005.[3] K. A. Hartholt, E. F. van Beeck, S. Polinder, N. van der Velde, E. M.van Lieshout, M. J. Panneman, T. J. van der Cammen, and P. Patka,“Societal consequences of falls in the older population: injuries,healthcare costs, and long-term reduced quality of life,” Journal ofTrauma and Acute Care Surgery, vol. 71, no. 3, pp. 748–753, 2011.[4] S. Studenski, S. Perera, K. Patel, C. Rosano, K. Faulkner, M. Inzitari,J. Brach, J. Chandler, P. Cawthon, E. B. Connor, et al., “Gait speedand survival in older adults,” Jama, vol. 305, no. 1, pp. 50–58, 2011.[5] S. N. Robinovitch, F. Feldman, Y. Yang, R. Schonnop, P. M. Leung,T. Sarraf, J. Sims-Gould, and M. Loughin, “Video capture of thecircumstances of falls in elderly people residing in long-term care:an observational study,” The Lancet, vol. 381, no. 9860, pp. 47–54,2013.[6] A. H. Khandoker, S. B. Taylor, C. K. Karmakar, R. K. Begg, andM. Palaniswami, “Investigating scale invariant dynamics in minimumtoe clearance variability of the young and elderly during tread-mill walking,” Neural Systems and Rehabilitation Engineering, IEEETransactions on, vol. 16, no. 4, pp. 380–389, 2008.[7] B. W. Schulz, “Minimum toe clearance adaptations to floor surfaceirregularity and gait speed,” Journal of biomechanics, vol. 44, no. 7,pp. 1277–1284, 2011.[8] A. J. Campbell, M. J. BORRIE, G. F. SPEARS, S. L. JACKSON, J. S.BROWN, and J. L. FITZGERALD, “Circumstances and consequencesof falls experienced by a community population 70 years and overduring a prospective study,” Age and ageing, vol. 19, no. 2, pp. 136–141, 1990.[9] C. W. Tyler and C.-C. Chen, “Signal detection theory in the 2afcparadigm: attention, channel uncertainty and probability summation,”Vision research, vol. 40, no. 22, pp. 3121–3144, 2000.[10] F. A. Wichmann and N. J. Hill, “The psychometric function: I. fitting,sampling, and goodness of fit,” Perception & psychophysics, vol. 63,no. 8, pp. 1293–1313, 2001.

Robotic psychophysics rig to assess, diagnose and rehabilitate the
neurological causes of falls in the elderly

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Robotic psychophysics rig to assess, diagnose and rehabilitate the neurological causes of falls in the elderly

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