Journal Search Engine
Search Advanced Search Adode Reader(link)
Download PDF Export Citaion korean bibliography PMC previewer
ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.18 No.4 pp.638-646

Effects of Gender, Laterality, Reaching Location, and Body Posture on the Maximum Reach of Elders

Haruetai Lohasiriwat*, Noppamas Weingket
Industrial Engineering Department, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
Corresponding Author, E-mail:
May 7, 2019 September 20, 2019 October 18, 2019


The maximum reach envelope (MRE) is beneficial in a range of design areas. This research studied the effects of five reaching factors on the elder MRE including shoulder deviation on horizontal location (60° to 180°), shoulder deviation on the vertical location (-50° to 90°), body posture (sitting/standing), laterality (left/right), and gender (male/female).

Thirty Thai elders aged range between 60-75 years old were recruited to the study. Using the in-house design guide rail, test participants demonstrated their maximum voluntary reaching movement by moving his/her arm upward. For each trial, the plane was located to reflect different shoulder deviation. The motion capture device was used to track participant’s reach endpoints.

Generally, the most significant difference was found with gender and laterality (p-value < 0.00). Male has longer reach distance than female. Reaching with right hand is farther than left hand. Participants reach at greater distance in standing as compared to sitting (p-value = 0.001). Interaction effects between horizontal and vertical location was significant (p-value = 0.004). Typically, elders reached more upward than downward. Horizontal location shows to have effect only when accompany with extreme vertical reach. Male and female are found to have different reaching behaviour according to the reaching locations.



    Demographic information shows that the number of older individuals is growing. Thailand is currently ranked the third most rapidly aging population in the world. The number of people aged 60 and over in Thailand now stands at about 10.6 million (4.7 million males and 5.9 million females) which is approximately 16.06% of the total population (Department of Older Persons, 2018). It is also expected that the number will increase to 14 million by 2025. With aging, decline in capabilities and functions are inevitable. For example, muscle mass and its strength tend to reduce by 30%~50% while posture and movement changes are common (Jarutart, 2005). Therefore, elders usually have difficulties in performing activities of daily living. Facilities of housing such as storage shelves, cooking stove, and sink are usually not specifically designed to accommodate elderly populations. Positioning appliances too high or too low which required some awkward postures such as extreme reach or bending to the floor are typically found. In the past with Thai culture that values caring for older family members accompany with large or extended family size, older seniors usually get assist with routine activities by the younger members. However, cultural change has moved the society toward more single family type where more elders tend to live alone or only with their aging spouse. Nowadays, living independently becomes more common among elders with the assist of advanced technologies and supporting government policies (e.g., Botia et al., 2012;World Health Organization, 2002). Referring to ergonomics principle, the gap between demands on the person and the individual’s capabilities can be lessening through appropriate design which could range from housing facilities, appliances, and environment. In the other words, successful design relies on the understanding of elders’ capabilities and their limitation.

    Reaching is a human movement requiring strength and mobility of the upper extremity (i.e., shoulder, upper and lower arm, hand and wrist, and fingers). It is a common functional movement in many occupations and activities of daily life. Whether simplistic or complex in nature, reaching are frequent and commonly found in various workplaces and activities (Johnston, 2017). As early as 1934, there was the first study described workspace layouts through measurement of reaching in both horizontal and vertical location (Maynard, 1972). In this study, the normal and maximum reach areas were traced as an outline when a person reached to sweep across a surface. The traces were later determined as a suitable workspace area for workers. Then, a range of subsequent studies was conducted to gain more understanding of the reaching dimension and its influencing factors. The female was found to reach at shorter distances than male due to their smaller body size (Das and Grady, 1983). For young adults, the maximum reach envelope for the standing position was already significantly larger than the corresponding measure in the seated position (Sengupta and Das, 2000). The effect may be enhanced in the case of elders as a result of poorer body balance in standing posture. A general assumption of all reach measurements held in past research was that the left arm mirrors the right arm, and therefore data was only ever collected on the right side (Konz and Johnson, 1999). This assumption could be viable for a healthy young adult, but it is still in doubt with the case of the elders. One underlying to support the belief is that age and handedness factors have a significant interaction effect on the overall hand strength (Ellis et al., 1988). More recently, a study has found that though the arm length is the most dominant dimension to predict the individual’s reaching distance, some other anthropometric measures are also important including stature, slump eye height, thigh clearance, forearm-hand length, shoulder height and shoulder slump height (Behara and Das, 2011). Their investigation highlighted the complexity of human anthropometry in determining reach. To sum up, Figure 1 illustrates the previously studied factors related to individual’s reaching distance.

    Nowadays, with more anthropometric data available, taking into account the human body size is suggested when customized design for reaching activities. Unfortunately, most of the information used at present is only from static data of body dimension with population age ranging from 18-55 years (Smith and Tayyari, 2000). When it comes to design for aging, there is no gold standard in the ergonomics literature particularly reaching under dynamic mode and for individualized design. Therefore, the aim of this current research is to measure elders reach envelope in dynamic approach utilizing comprehensive 3D motion analysis system. Additionally, the possible influencing factors are tested to verify their effects on reaching distance. With the more understanding of aging reach characteristics and the influencing factors, the prediction model to estimate aging individual reaching distance could later be obtained.

    2. METHODS

    2.1 Participants

    Volunteers aged range were between 60-75 years old. There was the total of thirty participants including fifteen males and fifteen females. All participants reported as living independently in their own homes. All of them were in healthy condition with no major medical problems. All volunteers were able to walk without any assistance (i.e., walkers, wheelchairs, or canes). Demographic data of the population are shown in Table 1.

    2.2 Methodology

    2.2.1 Instruments

    General information regarding health status and daily activities were collected using interview method with a fillin form. Participant’s abilities to perform daily activities are the main focus including grooming (i.e., brushing tooth, combing hair, face washing, shaving), dressing, eating with utensils, transferring between locations, changing postures between sitting and standing. Static anthropometry data were collected using anthropometer set, grid chart, and analog weight scale (Table 2).

    The dynamic reach distance was collected utilizing the 3D motion analysis system similar to the previous study measuring reach envelopes of sample adults (Kozey and Mackenzie, 2002). The motion analysis system used in the current study is OptitrackTM, which is able to detect movement in the area of 4.5m x 3.9m. The system consists of twenty hi-speed cameras and a set of spherical reflective markers to be attached to participant’s body landmarks. Also, to control movement direction, a vertical curved guide rail was built in-house and installed in the laboratory.

    2.2.2 Experimental Design

    There were five independent variables in the study include gender (male, female), laterality (right, left), body posture (standing, sitting), reach location on horizontal location (60°, 90°, 120°, 150°, 180°), and reach location on vertical location (-50° to <-20°, -20° to <10°, 10° to <40°, 40° to <70°, 70° to 90°)

    Dependent variable was the estimated reach distance in cm. Given with data collected from the motion analysis system including shoulder and knuckle locations (Figure 2), the reach distance was estimated from equation (1) below.

    r 2 = x 2 + y 2

    When; x = rcosθ, y = rsinθ

    Data from motion analysis system was exported at thirty frames per second and filtered using Loss-pass Butterworth method at 7 Hz frequency for data smoothing. The filtered data was later averaged within the predefined vertical location intervals.

    For analysis, main and interaction effects of all five independent measures on the estimated reach distance were tested using generalized linear models (GLM) with post-hoc comparisons. A statistical package (SPSS24) and Microsoft excel 2016 were used for analysis.

    2.2.3 Data Collection

    Upon the actual measurement, reflective markers were placed on the participant’s body landmarks. The four markers were used to locate right and left shoulders, right and left knuckles. Data collection steps were verbally instructed by the experimenter. The participant was located to have his/her tested shoulder above the centre of the designated spot placed next to the guide rail (Figure 3). The designated spot is the cutting point of six straight lines on the floor which are used as the reference point to identify the degree of shoulder deviation (Figure 4). Standing and Sitting as well as laterality factors were randomized.

    After the experimenter visually observed that participant’s tested shoulder was in-line with the reference point, participant was asked to perform the reaching task starting from arm hanging down to his/her body side, followed by continually moved his/her arm upward until maximum reach. Note that hand movement was continually guided by ensuring that the light weight stick always stays on the guide rail (Figure 5). The same collection procedure was repeated for the different shoulder deviation conditions by allowing participant to stand facing in different predefined directions. Trace of the hand movement for each trial was recorded using the motion analysis system. Data was later analysed offline.

    3. RESULTS

    3.1 Static Anthropometric Data

    Participants anthropometric data is summarized in mean, standard deviation, and data range (Table 3).

    3.2 Reach Envelop

    As the motion analysis system is used to trace the participant’s reach movement, raw data collected from the system illustrate the actual movement path. Figure 6 (left) gives an example of the subject’s left arm reach range in cm on the vertical location at five horizontal locations (i.e. 60°, 90°, 120°, 150°, 180°). The raw data is averaged into five vertical location intervals which later used during data analysis. Figure 6 (right) demonstrates an example of the same data but averaged into five vertical location intervals.

    3.3 Statistical Analysis

    Using general linear model method, five main effects and their interactions are tested for significant effect on reach distance. Table 4 presents the analysis results. Only 2-way interactions are reported since all other levels of interaction are found insignificant. In this study, there were greater amount of non-missing cases and therefore increasing the homogeneity of error variances (Levene’s F(199, 2495) = 1.151, p-value = 0.080). Participants were nested within the group factor in order to complete this analysis. The significant factors were accepted for predicting the absolute reach differences at R-square 0.39.

    Generally, significant main effects on mean reach distance were found with all tested factors except the location on horizontal location (Table 4). Gender, laterality, and location on vertical location were among the strongest significant factors (F(1, 2695) = 555.027, p-value < 0.000; F(1, 2695) = 172.254, p-value < 0.000; F(4, 2695) = 195.064, p-value < 0.000, respectively). Body posture was found to be significant at F(1, 2695) = 10.364, p-value < 0.001. Figure 7a to 7d demonstrate significant difference in main effects

    Five interactions effects were significant on mean reach distance including gender by laterality (F(1, 2695) = 52.487, p-value < 0.000), body posture by laterality (F(1, 2695) = 6.998, p-value < 0.008), vertical location by horizontal location (F(4, 2695) = 2.88, p-value < 0.003), gender by horizontal location (F(4, 2695) = 5.04, p-value < 0.000), and laterality by horizontal location (F(4, 2695) = 2.88, p-value < 0.003). Figure 8a to 8e demonstrate significant differences in interaction effects.


    As one might expect, male clearly demonstrates longer reach distance (56.07 ± 4.00 cm) than female (52.75 ± 3.60 cm). In this current study, the difference in reach between genders is approximately at 6% compared to 13.5 % in the previous study (Sengupta and Das, 2000). Larger body size (i.e., longer arm) in male is expected to be the major reason. Also, participants reach at slightly greater distance in standing posture (53.51 ± 4.27 cm) as compared to sitting posture (53.60 ± 4.61 cm). Though this finding is similar to the earlier finding (Sengupta and Das, 2000) which reported more distance in standing reach than sitting reach, their difference was much greater at 4.1cm and 6.2cm for the males and females respectively. They claimed that more constrained in sitting posture and change in spinal curve could be the reason. However, because the seat used in this present study has no backrest, constraint to the participant’s posture maybe lower and result in smaller difference than the referred study

    Reaching with right hand (54.63 ± 4.16 cm) results in further distance than with left hand (53.26 ± 4.38 cm). This finding is considered contradict to the prior believe which usually assumes the mirror image between right and left side of arm reaching (Konz and Johnson, 1999). The underlying reason could be the result of dominant and non-dominant hands. Having used more often and gaining more strength, the dominant hand could have a slower rate of musculoskeletal systems deterioration which leads to larger range of motion at the older age (Koley and Singh, 2010;Milanović et al., 2013). At the same time, participants may have higher ability and more willing to reach with his/her dominance arm. Note that the discrepancies in reach between right and left hand sides are more pronounced in female than male and also larger affect in sitting than standing conditions (Figure 8a and 8b).

    In terms of reaching direction, Figure 7c and Figure 8c clearly shows that elders consistently reach further upward than downward. However, when taking the horizontal locations into account, interaction effect between vertical and horizontal locations is evidenced. Reach distance increases with more horizontal abduction when reaching around the highest (70° to 90° vertically) and lowest (-50° to < -20° vertically) areas. On the contrary, the opposite trend (decrease distance with more horizontal abduction) is found for other vertical locations (from -20° to < 70°). The underlying reason for this finding is unclear. Gender effect could be a possible reason. Figure 8d suggests that decreasing reach distance along more horizontal abduction is rather common in females but not in males.

    In any circumstances, either increasing or decreasing in reaching distance, the finding implied that individual’s reach distances in the spectrum of a workspace are varied. Reach estimation using only static anthropometric data such as arm length could result in large error. Hence, the current study supports the use of distinct equations to separately predict reach distance at various reaching directions. This method has repeatedly introduced by many prior researches. For example, using six static anthropometric data, 78 regression equations were proposed to predict 78 reaching areas (6 vertical locations x 13 horizontal locations) (Behara and Das, 2011). Likewise, the earlier research has proposed 24 equations to predict reach distance in 24 locations (4 vertical locations x 6 horizontal locations) using only two structural dimensions including elbow-fingertip and shoulder-elbow lengths (Stoudt, 1973).

    Last but not least, though all independent factors significantly affect reach distance, each factor may not be equally important. Considering means differences, gender shows to be the most significant factor followed by location on vertical location, laterality, and body posture.


    This research investigated elders reach envelop from thirty Thai older adults utilizing motion capture analysis. The objective is to measure elders reach envelope in dynamic mode and to validate factors to be included in the reach prediction model. In conclusion, all hypothesized factors are found to have significant effect either directly or by interaction with other factors. The list of factors and their effects include;

    1. Gender: male reached further than female.

    2. Laterality: right hand reached further than left hand

    3. Body posture: individuals reached in standing posture further than sitting posture.

    4. Reaching location:

      • 4.1 Vertical location: Individuals reached upward further than downward

      • 4.2 Horizontal location: Male reached further with more shoulder abduction whereas female reached further with more shoulder adduction.

    For future study aim toward establishing the reach prediction model, it is suggested that all the above factors should be taken into account. Preferably, all factors should be assembled simultaneously. However, the body posture factor may also be abandoned from the model owing to its lowest significance and slight mean difference.


    The authors thank Magnolia Quality Development (MQDC) for the sponsorship in this study



    Influening factors of maximum voluntary reach distance.


    Calculation parameters for reach distance measured from shoulder to knuckle.


    Participant’s standing (left) and sitting (right) postures.


    Shoulder deviation on vertical (left) and horizontal (right) location.


    The guide rail and a light weight stick to assist in guiding the reach direction


    Reach distances on the vertical location at five horizontal locations; raw data (left), averaged data (right).


    Main effect plots on significant factors; (a) laterality, (b) gender, (c) vertical location, (d) body posture


    Interaction plots on significant factors; (a) laterality x gender, (b) laterality x body posture, (c) horizontal location x vertical location, (d) horizontal location x gender, (e) laterality x horizontal location.


    Descriptive statistics of the demographic data

    Anthropometric data measured in the study with its description and instrument used

    Summary statistics of the participants anthropometric data (cm)

    Effects of gender, body posture, laterality, horizontal and vertical reach locations on reach distance


    1. Behara, D. N. and Das, B. (2011), Anthropometric modelling for the determination of 3-D maximum functional reach, Theoretical Issues in Ergonomics Science, 12(1), 87-107.
    2. Botia, J. A. , Villa, A. , and Palma, J. (2012), Ambient assisted living system for in-home monitoring of healthy independent elders, Expert Systems with Applications, 39(9), 8136-8148.
    3. Das, B. and Grady, R. M. (1983), Industrial workplace layout design an application of engineering anthropometry, Ergonomics, 26(5), 443-447.
    4. Department of Older Persons (2018), Statistical data of the Thai elderly population. In Thai, 2019 Oct 11, Available from:.
    5. Ellis, S. J. , Ellis, P. J. , and Marshall, E. (1988), Hand Preference in a normal population, Cortex, 24(1), 157-163.
    6. Jarutart, T. (2005), Study on minimum standards of housing and the environment of the elderly, Chulalongkorn University, Bangkok, Thailand.
    7. Johnston, H. A. (2017), Measurement of the maximum reach envelope in persons with and without shoulder injury, Master’s thesis, Dalhousie University, Halifax, Canada.
    8. Koley, S. and Singh, A. P. (2010), Effect of hand dominance in grip strength in collegiate population of Amritsar, Punjab, India, Anthropologist, 12(1), 13-16.
    9. Konz, S. A. and Johnson, S. (1999), Work Design: Industrial Ergonomics, Holcomb Hathaway, Publishers; Inc., Scottsdale, AZ.
    10. Kozey, J. and Mackenzie, S. (2002), Considerations related to modelling the maximum reach envelope (MRE) as a sphere, Proceedings of the XVI Annual International Occupational Ergonomics and Safety Conference, Nova Scotia, Canada.
    11. Maynard, T. (1972), Ergonomics in Machine Design, New York.
    12. Milanović, Z. , Pantelić, S. , Trajković, N. , Sporiš, G. , Kostić, R. , and James, N. (2013), Age-related decrease in physical activity and functional fitness among elderly men and women, Clin Interv Aging, 8, 549-556.
    13. Sengupta, A. and Das, B. (2000), Maximum reach envelope for the seated and standing male and female for industrial workstation design, Ergonomics, 43(9), 1390-1404.
    14. Smith, J. and Tayyari, F. (2000), Occupational Ergonomics: Principles and Application, Chapman and Hall, London.
    15. Stoudt, H. W. (1973), Arm lengths and arm reaches: Some interrelationships of structural and functional body dimensions, American Journal of Physical Anthropology, 38(1), 151-161.
    16. World Health Organization (2002), Active Ageing: A policy Framework, Noncommunicable Disease Prevention and Health Promotion, Ageing and Life Course, Geneva, Switzerland.