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Introduction to Physiology and Biomechanics Practical Laboratory Answer Book
Figure 1. Maximum heart rate prediction using traditional and Gellish equation.
The age-predicted HRmax equation is commonly used as a guide during diagnostic exercise testing and in prescribing exercise programs. The traditional equation:
HRmax = 220 – age
is widely used, however the alternative equation exists, postulated by Gellish et al. (2007):
HRmax = 207 – 0.7 × age.
In Figure1, maximum heart rate (HRmax) predictions for an exercise participant between 18-80 years were calculated using both HRmax age-prediction equations: traditional (blue line), and Gellish (red line). It can be seen that predictions are similar in the middle age range, but differ markedly for younger and, even more, for older people.
Table 1 exercise intensity targets using different scales
|Very light||Light (bottom)||Light (top)||Moderate (bottom)||Moderate (top)||Vigorous (bottom)||Vigorous (top)||Very Vigorous||Max|
The Figures 2 and 3 depict the target heart rates of two example exercise participants calculated for the full range of exercise intensities outlined in table 1. The participants resting heart rates differ markedly and it is has consequences for their heart rates during the exercise, as can be seen in the figures. Although the predicted maximum heart rates for each type of exercise are identical for both participants, when Heart Rate Reserve is calculated accounting for their different resting heart rates, it is clear, that Participant 1, with a low RHR of 50 bpm has a much wider margin of HRR compared to the Participant 2, with a high RHR of 95 bpm. It has been shown in literature, that the HRR is linearly correlated with the VO2max, therefore for the Participant 2, it will be much more difficult to achieve a desired exercise intensity.
|Area of body||Name of bones||Number of bones|
|Mid foot||Cuneiform, Navicular, Cuboid||3|
|Pelvis||Pubis, Ischium, Ilium, Iliac Crest, Greater Trochanter||5|
|Lower back||Sacrum, Lumbar Vertebrae||2|
|Mid back||Thoriac Vertebrae||1|
|Shoulder blade||Spine of Scapula||1|
|Upper Arm||Humerus, Elbow||2|
|Palm||Trapezium, Scaphoid, Luminate, Trapezoid, Capitate, Triquetrum||6|
Complete the tables below:
|Image label||Ligament Name|
|Image label||Tendon Name|
|Image label||Ligament Name|
|Image label||Tendon Name|
|C||Iliotibial Band Tendon|
|Ankle||The ankle joint is formed by three bones:- Tibia
The ankle is a hinge joint
|– Dorsiflexion- Plantarflexion
|Scapulothoracic||The Scapulothoracic joint is an articulation of the Scapula, and is and independent joint, as the Scapula is attached to the Acromion Process by the lateral side of the joint.
Works with Scapula and Thoriac Vertebrae.
|– Upwards rotation- Downwards rotation
– Internal rotation
– External rotation
|Wrist/carpal bones||The Wrist/Carpals joint is the synovial joint. The Carpals create a surface which articulates the radial discs.||– Flexion- Extension
|Shoulder||The Shoulder joint is the most flexible joint in the human body. It has four main joints:- Sternoclavicular
The Shoulder is a ball and socket joint.
|– Flexion- Extension
– Medial/Lateral rotation
|Humoradial||It is a synovial hinge that opens and closes. The Trochea is an articulating surface to the Humerus. The contact point is the Humerus and Una.||– Flexion- Extension
Range of motion limited due to Ulna.
|Carpometacarpal of first metacarpal joint (thumb)||The Carpometacarpal joint is a Saddle Joint. It is a part of the carpal that controls the thumb. It is vital for controlling the hand.||– Flexion- Extension
Name ADAM DOB 25/10/1995 Height 1.6 m Body Mass 72 Kg
|Resting HR||Trial 1||Trial 2||Trial 3||Mean||Classification||Reference (normative data)|
|Cardiovascular test (BPM post 15secs)||69 BPM|
|1 minute press up||24||The aim is to achieve as many press ups as you can within 1 minute||Below average|
|Strength – grip||40||45||45||100||Using the HGD* client tested his strength within his arm||Average score on Norm|
|Flexibility||18||20||20||44.67||Performing a sit and reach test, the flexibility was tested in the lower back/hamstrings||Excellent score on Norm|
*Hand Grip Dynamotor
(Show your calculations)
Participant’s age: 21 years
RHR: 69 bpm
HRmax = 207 – 0.7 × 21 = 192.3
Using Uth–Sørensen–Overgaard–Pedersen VO2max can be estimated as:
VO2max = 15 × HRmax/ RHR = 15 × 192.3/69 = 41.8 ml/(kg×min)
Maximal oxygen uptake is a good measure of the cardiovascular fitness of and individual and their aerobic efficiency. The average VO2max values for the untrained healthy male are within the range of 35-40 ml/kg*min. The result obtained above is minimally above this range thus indicating that the individual’s aerobic fitness can be termed as average, and it is assumed that the subject does not train extensively any sport discipline.
VO2max, (from: maximum oxygen volume) also termed maximal aerobic capacity, or maximal oxygen consumption) is the maximum rate of oxygen consumption as measured during incremental exercise at sea level, and is a measure of the optimum volume of oxygen that an athlete can consume. It is described in either absolute value (millilitres per minute) or a relative value of ml per minute per kg of body weight (ml/kg/min).
Estimating VO2max allows evaluating the athlete’s maximum capacity for aerobic work, and as such can provide and important indication as to the subject’s cardiovascular condition. Thus, measuring the VO2max can help evaluate progress and design improvements to the exercise regime.
Disadvantages of the VO2max measurement include a potential risk for the subjects which are not healthy individuals and suffer from respiratory and cardiovascular problems (even indirectly, as a result of other illnesses). Additionally, an accurate measurement requires a considerable effort on the part of the subject, making the test quite elaborate and time consuming. Also, the VO2max value allows assessing oxygen consumption by the whole body, which does not provide any information as to the oxygen consumption by the particular groups of muscles in the body.
The goal of exercise prescription should be to successfully in design a range of exercises that would address subjects needs and concerns in a way that motivates the subject to adhere to the plan this helping them achieve their goals. Using the results from the table above a range of exercises could be prescribed to address some weaknesses revealed (particularly in the press up exercise) however there are limitations to the effectiveness of such approach. The results represent very narrow view of participant’s physical abilities, thus some of the needs could be neglected if only that data was used as a basis of prescription. No testing was done on the subject’s fitness while performing aerobic activity, thus it would be difficult to design a balanced program of aerobic and strength exercises (Knuttgen, 2007). Moreover, even with a suitably tailored prescription, participant’s willingness to comply with the plan is only assumed, rather than discussed with the subject. Perhaps the participant should be first informed on the benefits of exercise, and the risks of the lack of thereof, before an action plan can be suggested (Aspenes et al., 2011; Jeon et al., 2007)).
Knuttgen HG. Strength training and aerobic exercise: comparison and contrast. J Strength Cond Res. 2007 Aug. 21(3):973-8.
Aspenes ST, Nauman J, Nilsen TI, Vatten L, Wisløff U. Physical Activity as a Long Term Predictor of Peak Oxygen Uptake: The HUNT-Study. Med Sci Sports Exerc. 2011 Feb 28.
Jeon CY, Lokken RP, Hu FB, van Dam RM. Physical activity of moderate intensity and risk of type 2 diabetes: a systematic review. Diabetes Care. 2007 Mar. 30(3):744-52.
Upper body strengthening exercises would be recommended for this individual, particularly focusing on the development and strengthening of chest, arms and shoulders muscles as well as the upper back and core strengthening exercises. Aerobic training could also be recommended to improve general cardiovascular fitness of the subject.
Raw data record the skinfold results below (B-bicep, T-triceps, SC- subscapularis, SI-suprailiac)
|First 4 measures||Second measures||Third measures|
|ParticipantMale or Female||B 1||T 1||SC 1||SI 1||B 2||T 2||SC 2||SI 2||B 3||T 3||SC 3||SI 3||Total (mm)|
Raw data record for BMI calculations
|Participant||Height (m)||Weight (Kg)||BMI (Kg/m2)|
Completion of tables 1 & 2 (16 marks)
Table 3 Classification of body composition from skinfolds and BMI
|Participant||BMI (Kg/m2)||Classification||Fat %||Classification|
|1 M||25.5||overweight||16.4||Not obese|
|2 F||21.2||normal weight||27.8||Not obese|
|3 F||24.6||normal weight||26.5||Not obese|
|4 F||27.4||overweight||27||Not obese|
|5 M||27.2||overweight||19||Not obese|
|6 M||24.6||normal weight||16.4||Not obese|
|7 F||26.8||overweight||29.5||Not obese|
|8 M||27.4||overweight||16.4||Not obese|
For the classification of body fat %:
Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Gallagher D1, Heymsfield SB, Heo M, Jebb SA, Murgatroyd PR, Sakamoto Y. Am J Clin Nutr. 2000 Sep;72(3):694-701.
No more than 250 words, Please use at least 2-3 references to support your answers
One of the benefits of using BMI combined with the skinfold measurement is the simplicity of this method. The quick and easy way in which these tests can be performed makes them particularly suitable for subjects such as babies, for whom other methods could prove too cumbersome or enduring. Skinfold thickness data, when used as raw values, can provide reliable indicators of relative regional fatness, which can be evaluated over time in longitudinal studies, and BMI can be similarly used to analyse relative weight, particularly when combined with age and sex data on the subjects. Both methods are also relatively reliable in terms of reproducibility of results and are not very susceptible to measurement errors.
The imitations include the inability to differentiate between fat and lean body mass using BMI. Skinfold measurement is also notoriously inaccurate and unreliable in obese children (Tanner et al., 1975). Moreover, they assess only the subcutaneous fat, other fat deposits are not considered by the technique. Because the BMI and skinfold measurement are more reliable as the tools for a relative composition, a considerable weakness of both methods lies in the lack of wide ranging population studies, particularly among children, which would help to provide information of the changes in body composition in populations over time (Cole et al., 1995; Vansant et al., 1994).
What do the results suggest about the participants body fat/health/fitness
The results of the study presented above show that by measuring BMI, 5 out of the 8 participants can be qualified as overweight, which is defined by a BMI ≥ 25. Body fat percentage has not yet been classified reliably as to the range of values that are accepted to be normal. The most commonly used threshold is the value of ˃25 for men, and ˃35 for women, when the person is being described as obese (rather than “not obese” for the values below that thresholds). Based on the body fat %, none of the participants have been classified as obese.
Some other methods of assessing body composition which could be used are Bioelectric impedance analysis (BIA; Parker et al., 2003) and dual energy x ray absorptiometry (DXA). Both can offer much more reliable and direct assessment of body composition, although these the accuracy may be affected by such variables as age and body shape.
Revised standards for triceps and subscapular skinfolds in British children. Tanner JM, Whitehouse RH Arch Dis Child. 1975 Feb; 50(2):142-5.
Body mass index reference curves for the UK, 1990. Cole TJ, Freeman JV, Preece MA Arch Dis Child. 1995 Jul; 73(1):25-9.
Assessment of body composition by skinfold anthropometry and bioelectrical impedance technique: a comparative study. Vansant G1, Van Gaal L, De Leeuw I. JPEN J Parenter Enteral Nutr. 1994 Sep-Oct;18(5):427-9.
Validity of six field and laboratory methods for measurement of body composition in boys. Parker L, Reilly JJ, Slater C, Wells JC, Pitsiladis Y Obes Res. 2003 Jul; 11(7):852-8.
Evaluation of Lunar Prodigy dual-energy X-ray absorptiometry for assessing body composition in healthy persons and patients by comparison with the criterion 4-component model. Williams JE, Wells JC, Wilson CM, Haroun D, Lucas A, Fewtrell MS Am J Clin Nutr. 2006 May; 83(5):1047-54
Complete the table below
|Joint||Muscle(s)||Class of lever|
|Elbow|| BrachialisBiceps brachiiBrachioradialis
Flexor hallucis posterior
Flexor digitorium longus
|Ankle|| Tibialis anteriorExtensor digitorum longusExtensor hallucis longus
|Hip|| IliopsoasTensor fasciae lataeRectus femoris
|Hip|| Gluteus maximusSemitendiunosusSemimembranosus
Biceps femoris (long head)
Adductor magnus (ischial fibers)
|Shoulder|| Deltoid (anterior)Deltoid (lateral)Pectoralis major (clavicular head)
Biceps brachii (short head)
|Shoulder|| Latissimus dorsiDeltoid (posterior)Pectoralis major (sternal head)
Triceps brachii (long head)
Observational Gait Analysis in Healthy Volunteers – Laboratory Report
Gait analysis is a systematic study of human locomotion, comprising the measurement, description and analysis of the characteristics of this type of human motion. Research on gait analysis, pioneered by Aristotle in his work “On the Gait of Animals”, has been conducted systematically since the late 19th century; however the widespread application started only with the introduction of multi-camera motion capture systems, motion platforms capable of measuring ground-reaction forces, motion sensors and subsequently, a wide range of wearable sensors (Tao et al., 2012, Kim and Eng, 2004, Bonato, 2003).
The method allows to determine detailed parameters of human gait and to quantitatively evaluate the musculoskeletal functions of the body. Hence, gait analysis has been widely employed in health diagnostics, orthopaedics and rehabilitation, to monitor patient’s healing process, and in sports training, to study and improve athlete’s performance. Gait analysis is thus a powerful tool that can help researchers and clinicians to understand human motion and to inform the development of rehabilitation techniques for a range of pathological conditions, for example osteoarthritis and spine disorders; neuromuscular disorders such as stroke, Parkinson’s and Huntingdon’s diseases or cerebral palsy; and motion pathologies that are inherent to the ageing process (Jung et al., 2016, Turcot et al., 2008, Salarian et al., 2007). Gait analysis can provide valuable information to help support amputation patients in recovery and to aid with prosthesis fitting (Griffet, 2016). Poor gait performance can also aid researchers in predicting the prognosis for a range of other conditions, for example dementia (Beauchet et al., 2016). Additionally, the analysis of gait as a unique biometric modality that identifies people is being developed to use as a tool for the human identification by gait recognition (Sandau, 2016).
There are several features that can be measured to characterise and analyse human gait. Human walking is a cyclic movement of the parts of the body and thus involves repetitive motions. A normal walking gait cycle (GC) can thus be divided into eight phases: the stance period, encompassing the initial contact, loading response, midstance and terminal stance; and the swing period, encompassing the pre-swing, initial swing, mid-swing and terminal swing (Tao et al., 2012), as depicted in Figure 1.
Figure 1. Gait phases in a normal gait cycle: (a) the stance period; (b) the swing period. Reproduced from (Tao et al., 2012).
The initial contact and the terminal stance are the periods when both limbs are in contact with the ground, which is termed the double stance, although the limbs do not share the load equally during those periods. Generally, each double stance accounts for 10% of the GC, while single stance is typically 40%, and the swing phase is the remaining 40% of the gait cycle; although the exact proportions vary with the speed of walking, with the percentage of stance decreasing as the velocity of gait increases (Drewes et al., 2009). The whole gait cycle is equivalent to a stride (or more precisely, stride time), which can be defined as the time between sequential floor contacts (“foot contacts”) by the same limb. The stride length is thus a distance that one part of foot travels between two consecutive foot contacts. A step is the period between the sequential foot contacts by the opposing limbs, and the step length is a distance that one part of foot covers during each step. The stride length is thus a sum of the lengths of the left and right steps. The duration of gait cycle is commonly characterised by a cadence, which usually refers to the number of steps taken per minute, although it may also be presented as a number of strides per minute. These basic characteristics can be used to analyse gait in terms of symmetry and efficiency, and help to identify pathologies in walking movement.
The aim of this study was to analyse the gait of the group of healthy volunteers in normal conditions as compared to “discomfort” conditions, where the injury of the subject was simulated by adding the weight to one leg, and to study the effect of a discomfort due to the hypothetical injury on the gait characteristics of studied individuals.
Subjects of this study consisted of a sample of 42 healthy volunteers. The population data (mass and height) of the volunteers were collected. The mean height of the volunteers was 1.7 metre, with a standard deviation (SD) of 0.11 m. The mean weight of participants was 71.3 kg, with SD of 13.4 kg. The participants were asked to walk, in comfortable clothing, over a 10 m distance in the laboratory setting. Each participant was observed by a group of four students, noting and recording the number of steps taken over the entire 10 m distance. The total time in which the 10 m distance was covered was measured with a timer and recorded by another student. The experiment was performed twice, in normal and in “discomfort” conditions. The “discomfort” experimental setup constituted placing a weight on the participant’s left shoe, thus simulating the injury of the subject. Following data acquisition, the step length, the stride length, the velocity of walk and the gait cadence were calculated; and the results were analysed and the one-way analysis of variance (ANOVA) was performed using the Excel data analysis software of the Microsoft Office suite.
In this study, the effect of a walking discomfort due to an “injury”, simulated by placing a weight on the participant’s left shoe, was considered. The data acquired in the walking experiments in both “normal” and “discomfort” experimental setup have been recorded and summarised in Table 1. From the variables recorded during the study, the mean total walking times and the mean numbers of steps taken were calculated, together with standard deviations. Subsequently, step length values were calculated as a distance/number of steps, and the stride lengths as double the step lengths. Velocity was calculated as a distance/total time, and the cadence was determined as a number of steps per minute of walk.
Table 1. Top: mean values (with standard deviations, SD) of the total time of a walk and the number of steps recorded over the 10 m distance. Bottom: mean values (with standard deviations, SD) of the step length, stride length, walk velocity and cadence calculated for the study participants.
|Conditions||Normal(mean ± SD)||Discomfort(mean ± SD)|
|Time (s)||7.339 ± 1.027||8.43 ± 2.616|
|Number of steps||14.345 ± 1.52||15.238 ± 2.325|
|Step length (m)||0.704 ± 0.073||0.67 ± 0.095|
|Stride length (m)||1.409 ± 0.148||1.34 ± 0.19|
|Velocity (m/s)||1.386 ± 0.177||1.261 ± 0.27|
|Cadence (steps/min)||118.34 ± 12.429||112.243 ± 15.448|
The one-way analysis of variance (ANOVA) was used to test whether there was a significant difference between the results of the “normal” and “discomfort” experimental setup. The results of the statistical analysis were presented in Table 2. The analysis revealed that, as the calculated F ratio 3.9726 was larger than the F crit value 3.9574, the difference between the participant’s mean cadence in the normal and “discomfort” conditions was statistically meaningful.
Table 2. ANOVA single factor statistical analysis of the difference between the results of the “normal” and “discomfort” experiment.
|Source of Variation||SS||df||MS||F||P-value||F crit|
The results of any study depend on the experimental conditions. It is therefore essential to consider limitations of a study before any conclusions can be drawn, and to interpret data in light of the experimental conditions. In this study, gait characteristics of the group of healthy volunteers were studied and compared in normal conditions, versus walking in “discomfort”, where an injury was simulated by adding the weight to participant’s left shoe. It has been observed, that in the case of an “injury, the gait parameters were affected, with participant taking more time, on average, to cover the same 10 m distance: 8.43 s as opposed to 7.339 s in normal conditions. The average number of steps has also increased under duress, albeit slightly, from 14.345 to 15.238. Participant in conditions of “discomfort” made on average shorter steps (0.67 m as opposed to 0.7m) and shorter strides of 1.34 m as opposed to 1.4 m. The velocity of walk was also observed to decrease, with mean values of 1.261 m/s, in contrast to 1.386 m/s in normal conditions. The cadence, which summarises the manner of walking of the participants, was decreased, with participants under duress making on average 112 steps per minute, while in the normal conditions they were making 118 steps in a minute. Moreover, statistical analysis of the data revealed that the change in cadence was indeed statistically significant, and thus represented a true change to the manner of walking; in “discomfort” conditions participants’ walking was no longer as effortless as in normal conditions. This is potentially and interesting contribution to the effects leg injury may have of the manner of walking, and can inform future interventions for the patients with injuries.
As a useful tool in biomechanical research and in the clinical practice, gait analysis is attracting great interest among researchers. In this study, gait characteristics of the group of healthy volunteers were studied and compared in normal and in simulated injury conditions. Data analysis revealed that there was a statistically meaningful difference in the gait cadence of participants between the two experimental setups. Analysis of gait characteristics in laboratory setting can inform the development of novel techniques of studying human locomotion, but can also provide the reference data for the normal versus restricted mode of walking, for other studies to consider. It is thus hoped, that this study may contribute to the better understanding of the human locomotion in restricted conditions, such as an injury, and provide valuable information on the changes to the gait characteristics when weights are used on the legs, as happens frequently in some sports training setups.
BEAUCHET, O., ANNWEILER, C., CALLISAYA, M. L., DE COCK, A. M., HELBOSTAD, J. L., KRESSIG, R. W., SRIKANTH, V., STEINMETZ, J. P., BLUMEN, H. M., VERGHESE, J. & ALLALI, G. 2016. Poor Gait Performance and Prediction of Dementia: Results From a Meta-Analysis. J Am Med Dir Assoc.
BONATO, P. 2003. Wearable sensors/systems and their impact on biomedical engineering. IEEE Eng Med Biol Mag, 22, 18-20.
DREWES, L. K., MCKEON, P. O., PAOLINI, G., RILEY, P., KERRIGAN, D. C., INGERSOLL, C. D. & HERTEL, J. 2009. Altered ankle kinematics and shank-rear-foot coupling in those with chronic ankle instability. J Sport Rehabil, 18, 375-88.
GRIFFET, J. 2016. Amputation and prosthesis fitting in paediatric patients. Orthop Traumatol Surg Res, 102, S161-75.
JUNG, T., KIM, Y., KELLY, L. E. & ABEL, M. F. 2016. Biomechanical and perceived differences between overground and treadmill walking in children with cerebral palsy. Gait Posture, 45, 1-6.
KIM, C. M. & ENG, J. J. 2004. Magnitude and pattern of 3D kinematic and kinetic gait profiles in persons with stroke: relationship to walking speed. Gait Posture, 20, 140-6.
SALARIAN, A., RUSSMANN, H., VINGERHOETS, F. J., BURKHARD, P. R. & AMINIAN, K. 2007. Ambulatory monitoring of physical activities in patients with Parkinson’s disease. IEEE Trans Biomed Eng, 54, 2296-9.
SANDAU, M. 2016. Applications of markerless motion capture in gait recognition. Dan Med J, 63.
TAO, W., LIU, T., ZHENG, R. & FENG, H. 2012. Gait analysis using wearable sensors. Sensors (Basel), 12, 2255-83.
TURCOT, K., AISSAOUI, R., BOIVIN, K., PELLETIER, M., HAGEMEISTER, N. & DE GUISE, J. A. 2008. New accelerometric method to discriminate between asymptomatic subjects and patients with medial knee osteoarthritis during 3-d gait. IEEE Trans Biomed Eng, 55, 1415-22.