Exploring Markets for Assistive Technologies for the Elderly

The Challenge

The world population will age dramatically by 2050. The increasing need for ambulant care and home health services places a growing strain on human caregivers, insurance companies, and social systems. New technologies are needed to support independent living for the elderly.

Method: Engineering Checklist

Analyzing data related to elder care, using sex and gender analysis, reveal new opportunities for assistive technologies and robotics. Researchers have studied the different needs of women and men as they age. This research along with collaboration with the elderly, their caregivers, and further stakeholders, provide engineers key insights for designing and developing assistive products that are useful to a broad user base.

Gendered Innovations:

  1. Assessing Women's and Men's Needs for Assistive Technologies
  2. Developing Assistive Technologies Considering Women’s and Men’s Needs
  3. Using Participatory Design to Create the Next Generation of Assistive Technology

The Challenge
Gendered Innovation 1. Assessing Women's and Men's Needs for Assistive Technologies
Method: Analyzing how Sex and Gender Interact
Gendered Innovation 2. Developing Assistive Technologies Considering Women’s and Men’s Needs
Method: Participatory Research and Design
Gendered Innovation 3. Using Participatory Design to Create the Next Generation of Assistive Technology

The Challenge

The proportion of the world population over age 60 is projected to increase from 11 percent today to 22 percent by 2050—see charts below (data from United Nations, 2012). Population pyramids, especially in Europe, the U.S. and Canada, reveal that the proportion of the elderly is increasing while that of young people is shrinking. As the population ages, more people will need care. At the same time, fewer people will be available to provide and pay for the patient-centered care characteristic of Western societies today. With fewer people available to provide care, new solutions will be needed. In recent years, researchers have developed technologies to assist in elder care (Peterson et al., 2012; Broekens et al., 2009).

population aged 60 and over

Gendered Innovation 1: Assessing Women's and Men's Needs for Assistive Technologies

Understanding the characteristics of elderly populations is key to designing successful assistive technologies. While elderly women and men often have similar needs, understanding how sex and gender interact to impact aging can assist engineers in developing technologies that best fit user needs. Studies show that sex and gender interact to impact health in old age.

  • Dementia strikes women and men in equal numbers as they age, but because women live longer in most developed countries, they suffer more dementia (Plassman et al., 2007; Nowrangi et al., 2011).
  • Arthritis is more common in women than in age-matched men, and rheumatoid arthritis occurs 2-3 times more often in women than in age-matched men (Alamanos et al., 2005; Linos et al., 1980).
  • Dexterity impairment impacts men more than age-matched women (Desrosiers et al., 1995).
  • Hearing impairment is more common among men than in age-matched women (Cruickshanks et al., 2010)—see chart. These differences may depend on sex-specific biology, but gendered divisions of labor also mean that men are more likely than women to be exposed to occupational noise (Engdahl et al., 2012).

age Cohort by incidence men vs women

Analyzing sex and gender is important for engineering successful assistive technologies, and this will become even more important as the population continues to age. Data from the U.S. reveal that the majority of the elderly are women, and women make up an increasingly large proportion of older people at more advanced ages. About 53 percent of the U.S. population aged 65-69 are women; this increases to 65-80 percent among the "oldest old," aged 85 and over—see chart below. These data require special attention: When research for assistive technologies is sex- and gender-blind, the marketability, usefulness, and acceptability of such technologies can be limited.

percent of women of age chort

Data also reveals important gender differences in partnering patterns, such as marriage age and age differences in partnerships. In Western societies, women tend to marry slightly older men. In England and Wales, where data are available, women on average marry men 2.6 years older; the most common age gap is one year (Bhrolcháin, 2005). Similar values are found in European Union countries and in the U.S. (Lakdawalla, 2003; Van Poppel et al., 2001). Marriage age gaps (a gendered phenomenon), combined with women’s greater longevity, mean that women are more likely to live alone than men. In the U.S., women make up 59 percent of people over the age of 65, but 76 percent of those living alone (Pew et al., 2004). Women are more likely than men to be widowed, and the death of a spouse is a major predictor of loneliness (Dragset et al., 2011). This may imply women have greater needs for assistive technologies that provide social connectivity.

mean age difference for married women at age 34-44

Similar patterns hold for same sex marriages and partnerships: In Sweden, where same-sex marriages and registered partnerships are recognized (and data are available), age gaps are on average larger among homosexual than heterosexual couples. For example, an age gap of 10 or more years exists in 34 percent of homosexual male partnerships/marriages, 15 percent of homosexual female partnerships/marriages, and 9 percent of opposite-sex marriages (Andersson et al., 2006). A demographic study in Norway finds that the average age difference in gay and lesbian registered partnerships was 7.0 years, compared to an average age difference of 2.5 years for heterosexual couples (Kristiansen, 2005; Noack et al., 2005).

Method: Analyzing how Sex and Gender Interact

arrow sex and gender needs drive new assistive technologies

Analyzing sex (physical needs) and analyzing gender (social needs) of the elderly, and analyzing how these physical and socio-cultural needs combine in individual women and men helps researchers design the most effective and marketable assistive technologies. Overall, designers should be aware that the majority of the elderly are women. Analyzing sex differences reveals that women and men often have distinctive needs for physical mobility, cognitive dexterity, etc. Gender differences in marriage age, partnering patterns, experience in household management, and receptivity to technology may be important to consider for effective design. When addressing the market for assistive technologies, researchers will want to take into account women's and men's specific needs as elderly and as elder caregivers.


Gendered Innovation 2: Developing Assistive Technologies Considering Women's and Men's Needs

This section presents examples of assistive technologies designed to respond to women’s and men’s needs as discussed above—with special attention to gendered innovations in design.

  1. 1. Visual assistance:

    The European Union's Framework Programme 7 (FP7) project, "Assisting Personal Guidance System for People with Visual Impairment" (ARGUS), is developing a system to promote safe, autonomous movement for visually-impaired people (Dubielzig et al., 2012). ARGUS is envisioned as a handheld device containing a global positioning system (GPS) receiver and wireless modem that will determine the location of the user and provide audio and haptic guidance allowing the user to follow pre-determined paths in both built and natural settings (Otaegui et al., 2012—see diagram.)


  2. Mobility assistance

    Wheelchairs are important mobility aids, and powered wheelchairs can allow mobility for people who lack the strength and/or dexterity to operate manual wheelchairs. However, some people cannot operate powered wheelchairs (Fehr et al., 2000). In recognition of this, researchers are developing a robotic wheelchair, "Wheeley," designed to perform semi-automatic navigation. Rather than controlling the wheelchair's fine movements manually, users are to be able to issue general voice commands ("turn left," "move forward," etc.). Computer vision systems then determine the exact path to be taken (Bailey et al., 2007).

  3. Cognitive assistance: Mental exercises may slow cognitive decline (Wilson et al., 2010). A number of technologies have been developed to keep minds sharp. These include:
    • The BrightArm Rehabilitation System, developed with support of the U.S. National Institutes of Health (NIH), monitors and trains arm and hand movements in users with stroke-related dementia (Rabin et al., 2012; Rabin et al., 2011). The NIH is also supporting development of a robotic exoskeleton for stroke patients. The exoskeleton is intended for both diagnostic and rehabilitative use, and will incorporate a brain-machine interface (NIH, 2012).
    • A 3-D game where users search for objects in a simulated apartment, developed at the Technische Universität Chemnitz (Lange et al., 2010; Sitzer et al., 2006).
    • A memory training game in which users are asked to memorize a shopping list and pick out items at a virtual convenience store, developed and tested in Hong Kong (Man et al., 2011).
    • grid picture of EldergamesThe EU's “Development of High Therapeutic Value Information Systems and Technology [IST]-Based Games for Monitoring and Improving the Quality of Life of Elderly People” (ELDERGAMES) uses virtual reality and specialized hardware to train a range of skills, such as memory, reasoning, and selective attention (Gamberini et al., 2009).

In addition to projects aimed at slowing the onset of dementia, the European Union’s Framework Programme 6 (FP6) project, “Helping People with Mild Dementia Navigate their Day” (COGKNOW), has developed prototype technologies to assist people with dementia in their daily activities. COGNOW can remind users of medical appointments and mealtimes, help users locate items such as keys, and support contact between the user, carers, and health professionals (Bresciani et al., 2008).

Method: Participatory Research and Design

Including the elderly—both women and men—in identifying and testing assistive technologies is key to success. Real users may offer insights that lead to innovation. For example, researchers established requirements for COGKNOW by including "persons with dementia from diverse backgrounds, e.g. gender, age and living conditions" (Bengtsson et al., 2009).

Research and design of assistive technologies also gain by including health professionals and caregivers. EU women are about twice as likely as EU men to provide informal care for ill or elderly adults (Daly et al., 2003). Researchers in the U.S. report similar findings—about 70 percent of informal care is provided by women (Lahaie et al., 2012; Kramer et al., 1995). Over the years, these care givers develop hands-on knowledge that technology designers can access have through participatory design (Landau et al., 2010). COGKNOW designers, for example, tapped into this knowledge by consulting women and men caregivers and with different relationships to dementia patients (sons, daughters, spouses, cousins, etc.) (Andersson et al., 2007). In Finland, researchers have studied both care recipients' and caregivers' responses to over 60 assistive technologies in four pilot "smart homes" (Melkas, 2012)—see also Rethinking Engineering Processes.

Gendered Innovation 3. Using Participatory Research and Design to Create the Next Generation of Assistive Technology

Studies show that assistive technologies developed with the participation of the intended users are accepted at a higher rate. Involving the users-to-be already in the early stages of development ensures that the technology fits their needs (Ghorbel et al. 2008; Kanis, 2011). McCreadie et al. found that when the assistive technologies are smart, simple, reliable, and meet a specific need, they are likely to be integrated into everyday life (McCreadie et al., 2005).

Many assistive technologies will be delivered as handheld, room-installed devices, etc. that simply work in the background. Some, however, may be robotic, working collaboratively with a smart home environment to address psychosocial conditions, such as isolation and depression. Robots may interact directly with users to monitor mental status, provide cognitive stimulation, companionship, and assist with navigating complex environments (Pollack, 2005). Tapus et al. defined some criteria for assistive technology used in physical interaction, which increase the acceptance of the user. These criteria are embodiment, personality, empathy, engagement, adaption and transfer (Tapus et al., 2007). Machines that interact with humans provide further design challenges:

  1. 1. Emotion: Definitions of artificial intelligence (AI) are changing as engineers recognize emotion not as a byproduct of intelligence but rather as a driving force behind cognition, attention, and learning (Minsky, 2007; Arbib et al., 2004). In developing emotional intelligence in robots, gendered differences in expression may become important (Brody et al., 2008). The CompanionAble robot expresses emotion via digitally-rendered “eyes” that appear on a screen (CompanionAble, 2010). picture of emotions on robot faces
  2. Ethnicity and Skin Color: Existing facial recognition algorithms—used to uniquely identify individuals, detect emotional cues, etc.—often show better performance for subjects of one skin color than another. For example, in an international competition, facial recognition algorithms developed by researchers in East Asian countries were more accurate for Asian faces than Caucasian faces, while algorithms developed in Western countries were more accurate for Caucasian faces than Asian faces (Phillips et al., 2011). Considering these variables may be important in developing robotic facial recognition systems for global markets.
  3. Culturally-Determined Sense of Personal Space: Cross-cultural differences in perceptions of personal space are well documented; a distance that is considered "standoffish" in one culture might be considered "pushy" in another (Fries, 2005). Research on cross-cultural standards of personal space may help the assistive robots interact with users in a socially-acceptable way.


Women and men differ in their needs for and experience with technology. Women may have less technical experience and less positive attitudes toward technology (Gaul et al., 2010). They may also be more apprehensive about using assistive technologies, such as robots, in domestic environments (Cortellessa et al., 2008). Thus it is important to include both women and men in technology design. Analyzing sex and gender as well as including both women and men users in technology development is a positive action that can lead to better designs and improve marketability of products.

Researchers are developing new assistive technologies to support independent living for the elderly and to lighten the burdens of caregivers. Through participatory research and design with both the elderly and their caregivers, designers have gained key insights for developing assistive products that are useful to a broad user base. Involving users and stakeholders in the design process enhances outcomes. Building machines that based on sex and gender analysis of demographic data will be important for the development of the next generation of assistive technology.

Works Cited

  • Academy of Medical Sciences (2012). Human Enhancement and the Future of Work. Report from a joint workshop hosted by the Academy Medical Sciences, the British Academy, the Royal Academy of Engineering and the Royal Society.
  • Alamanos, Y., & Drosos, A. (2005). Epidemiology of Adult Rheumatoid Arthritis. Autoimmunity Reviews, 4 (3), 130-136.
  • Andersson, G., Noack, T., Seierstad, A., & Weedok-Fekjær, H. (2006). The Demographics of Same-Sex Marriages in Norway and Sweden. Demography, 43 (1), 79-98.
  • Andersson, S., & Meiland, F. (2007). COGKNOW Field Test #1 Report. Brussels: European Commission.
  • Arbib, M., & Fellous, J. (2004). Emotions: From Brain to Robot. Trends in Cognitive Sciences, 8 (12), 554-561.
  • Bailey, M., Chanler, A., Maxwell, B., Micire, M., Tsui, K., & Yanco, H. (2007). Development of Vision-Based Navigation for a Robotic Wheelchair. Proceedings of the 10th Annual Institute of Electrical and Electronics Engineers (IEEE) International Conference on Rehabilitation Robotics, June 12th-15th, Noordwijk, The Netherlands.
  • Bengtsson, J., Castellot, R., Nugent, C., Dröes, R-M., Moelaert, F., Mulvenna, M., Meiland, F., & Haaker, T. (2009). COGKNOW: Final Report. Brussels: European Commission.
  • Bhrolcháin, N. (2005). The Age Difference at Marriage in England and Wales: A Century of Patterns and Trends. Population Trends, 120, 7-14.
  • Bresciani, M., & Lhoutellier, V. (2008). COGKNOW: Prototype Release of Multi-Modal Interfaces. Brussels: European Commission.
  • Brody, L., & Hall, J. (2008). Gender and Emotion in Context. In Lewis, M., Haviland-Jones, J., & Barrett, L. (Eds.), Handbook of Emotions, Third Edition, pp. 395-408. New York: Guilford Press.
  • Broekens, J., Heerink, M., & Rosendal., H. (2009). Assistive Social Robots in Elderly Care: A Review. Gerontechnology: International Journal on the Fundamental Aspects of Technology to Serve the Ageing Society, 8 (2), 94-103.
  • CompanionAble. (2010). CompanionLetter—Number 2.
  • CompanionAble. (2009). CompanionLetter—Number 1.
  • Cortellessa, G.,Scopelliti, M., Tiberio, L., Koch Svedberg, G., Loutfi, A., & Pecora, F. (2008). A Cross-Cultural Evaluation of Domestic Assistive Robots. Proceedings of AAAI Fall Symposium on AI in Eldercare: New Solutions to Old Problems. AAAI.
  • Cruickshanks, K., Zhan, W., & Zhong, W. (2010). Epidemiology of Age-Related Hearing Impairment. In Gordon-Salant, T., Frisina, R., Popper, A., & Fay, R. (Eds.), The Aging Auditory System, pp. 259-274. New York: Springer Science and Business Media.
  • Cruickshanks, K., Tweed, T., Wiley, T., Klein, B., Klein, R., Chappell, R., Nondahl, D., & Dalton, D. (2003). The 5-Year Incidence and Progression of Hearing Loss: The Epidemiology of Hearing Loss Study. Archives of Otolaryngology—Head & Neck Surgery, 129 (10), 1041-1046.
  • Daly, M., & Rake, K. (2003). Gender and the Welfare State: Care, Work, and Welfare in Europe and the USA. Cambridge: Polity Press.
  • Desrosiers, J., Hébert, R., Bravo, G., & Dutil, É. (1995). Upper Extremity Performance Test for the Elderly (TEMPA): Normative Data and Correlates with Sensorimotor Parameters. Archives of Physical Medicine and Rehabilitation, 76 (12), 1125-1129.
  • Dragset, J., Kirkevold, M., & Espehaug, B. (2011). Loneliness and Social Support among Nursing Home Residents Without Cognitive Impairment: A Questionnaire Survey. International Journal of Nursing Studies, 48 (5), 611-619.
  • Dubielzig, M., Classen, G., Lindemann, M., Görlich, J., Wegge, K., Patti, D., Marcoci, A., Otaegui, O., Loyo, E., & Foesleitner, C. (2012). ARGUS (Assisting Personal Guidance System for People with Visual Impairment): State of the Art of the Relevant Technologies and Standards. Brussels: European Commission.
  • Engdahl, B., Krog, N., Kvestad, E., Hoffman, H., & Tambs, K. (2012). Occupation and the Risk of Bothersome Tinnitus: Results from a Prospective Cohort Study (Nord-Trøndelag Health Study, HUNT). British Medical Journal, 2 (1), e000512.
  • England, P., & McClintock, E. (2009). The Gendered Double Standard of Aging in U.S. Marriage Markets. Population and Development Review—Data and perspectives, 35 (4), 797-816.
  • Fehr, L., Langbein, W., & Skaar, S. (2000). Adequacy of Power Wheelchair Control Interfaces for Persons with Severe Disabilities: A Clinical Survey. Journal of Rehabilitation Research and Development, 37 (3), 353-360.
  • Fries, S. (2005). Cultural, Multicultural, Cross-Cultural, Intercultural: A Moderator’s Proposal. Paris: International Association of Teachers of English as a Foreign Language (IATEFL) Publications.
  • Gamberini, L., Martino, F., Seraglia, B., Spagnolli, A., Fabregat, M., Ibanez, F., Alcaniz, M., & Andrés, J. (2009). Eldergames Project: An Innovative Mixed-Reality Table-Top Solution to Preserve Cognitive Functions in Elderly People. Proceedings of the Institute of Electrical and Electronics Engineers (IEEE) Second International Conference on Human Systems Interactions, May 21st—23rd, Canatia, Italy.
  • Gaul, S., Wilkowska, W., Ziefle, M. (2010). Accounting for User Diversity in the Acceptance of Medical Assistive Technologies. Proceedings of the 3rd International ICST Conference on Electronic Healthcare for the 21st Century, eHealth.
  • Ghorbel, M., Arab, F., and Mokhtari, M. (2008). Assistive Housing: Case Study in a Residence for Elderly People. In Pervasive Computing Technologies for Healthcare, pp. 140–143.
  • Howden, L., & Meyer, J. (2011). 2010 Census Briefs—Age and Sex Composition. Washington, D.C.: U.S. Census Bureau.
  • Kanis, M., Alizadeh, S., Groen, J., Khalili, M., Robben, S., Bakkes, S. & Kröse, B. (2011). Ambient Monitoring from an Elderly-Centred Design Perspective: What, Who, and How? Proceedings of the International Joint Conference on Ambient Intelligence (AMI-11), Amsterdam.
  • Kramer, B., & Kipnis, S. (1995). Eldercare and Work-Role Conflict: Toward an Understanding of Gender Differences in Caregiver Burden. The Gerontologist, 35 (3), 340-348.
  • Kristiansen, J. (2005). Age Differences at Marriage: The Times, They Are A Changing? Oslo: Statistics Norway.
  • Lahaie, C., Earle, A., & Heymann, J. (2012). An Uneven Burden: Social Disparities in Adult Caregiving Responsibilities, Working Conditions, and Caregiver Outcomes. Research on Aging, 1-32.
  • Lakdawalla, D., & Schoeni, R. (2003). Is Nursing Home Demand Affected by the Decline in Age Difference Between Spouses? Demographic Research, 8 (10), 279-304.
  • Landau, R., Auslander, G., Werner, S., Shoval, N., & Heinik, D. (2010). Families’ and Professional Caregivers’ Views of Using Advanced Technology to Track People with Dementia. Qualitative Health Research, 20 (3), 409-419.
  • Lange, B., Requejo, P., Flynn, S., Rizzo, A., Valero-Cuevas, F., Baker, L., & Winstein, C. (2010). The Potential of Virtual Reality and Gaming to Assist Successful Aging with Disability. In Jensen, M., Molton, I., & Kraft, G. (Eds.), Aging with a Physical Disability: Physical Medicine and Rehabilitation Clinics of North America. Amsterdam: Elsevier.
  • Linos, A., Worthington, J., O’Fallon, W., & Kurland, L. (1980). The Epidemiology of Rheumatoid Arthritis in Rochester, Minnesota: A Study of Incidence, Prevalence, and Mortality. American Journal of Epidemiology, 111 (1), 87-98.
  • Man, D., Chung, J., & Lee, G. (2011). Evaluation of a Virtual-Reality-Based Memory Training Programme for Hong Kong Chinese Older Adults with Questionable Dementia: A Pilot Study. International Journal of Geriatric Psychiatry, 27 (5), 513-520.
  • McCreadie C., & Tinker A. (2005). The Acceptability of Assistive Technology to Older People. Aging and Society, 25, 91-110.
  • Melkas, H. (2012). Innovative Assistive Technology in Finnish Public Elderly-Care Services: A Focus on Productivity. Work: A Journal of Prevention, Assessment, and Rehabilitation, Online in advance of print.
  • Minsky, M. (2007). The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind. New York: Simon and Schuster.
  • National Institutes of Health (NIH). (2012). NIH Announces National Robotics Initiative Awardees. Washington, D.C.: Department of Health and Human Services.
  • Noack, T., Seierstad, A., & Weedon-Fekjær, H. (2005). A Demographic Analysis of Registered Partnerships (Legal Same-Sex Unions): The Case of Norway. European Journal of Population, 21 (1), 89-109.
  • Nowrangi, M., Rao, V., & Lyketsos, C. (2011). Epidemiology, Assessment, and Treatment of Dementia. Journal of the Psychiatric Clinics of North America, 34 (2), 275-294.
  • Otaegui, O., Loyo, E., Carrasco, E., Fösleitner, C., Spiller, J., Patti, D., Olmedo, R., & Dubielzig, M. (2012). ARGUS: Assisting Personal Guidance System for People with Visual Impairment. Paper Presented at the 17th International Conference on Urban Planning, Regional Development, Information Society, and Urban / Transport / Environmental Technologies (REAL CORP)— Remixing the City: Towards Sustainability and Resilience?, May 14th-16th, Vienna.
  • Peterson, C., Prasad, N., & Prasad, R. (2012). The Future of Assistive Technologies for Dementia. Gerontechnology, 11 (2), 195-202.
  • Pew, R., & Van Hemel, S. (Eds.). (2004). Technology for Adaptive Aging. Washington, D.C.: National Academies Press.
  • Phillips, P., Jiang, F., Narvekar, A., Ayyad, J., & O’Toole, A. (2011). An Other-Race Effect for Face Recognition Algorithms. Association for Computing Machinery (ACM) Transactions on Applied Perception (TAP), 8 (2), 14-25.
  • Plassman, B., Langa, K., Fisher, G., Herringa, S., Weir, D., Ofstedal, M., Burke, J., Hurd, M., Potter, G., Wodgers, W., Steffens, D., Willis, R., & Wallace, R. (2007). Prevalence of Dementia in the United States: The Aging, Demographics, and Memory Study. Neuroepidemiology, 29 (1-2), 125-132.
  • Pollack, M. (2005). Intelligent Technology for an Aging Population: The Use of Artificial Intelligence (AI) to Assist Elders with Cognitive Impairment. AI Magazine, 26 (2), 9-24.
  • Rabin, B., Burdea, G., Roll, D., Hundal, J., Damiani, F., & Pollack, S. (2012). Integrative Rehabilitation of Elderly Stroke Survivors: The Design and Evaluation of the BrightArmTM. Disability and Rehabilitation: Assistive Technology, 7 (4), 323-335.
  • Rabin, B., Burdea, G., Hundal, J., Roll, D., & Damiani, F. (2011). Integrative Motor, Emotive, and Cognitive Therapy for Elderly Patients Chronic Post-Stroke: A Feasibility Study of the BrightArmTM Rehabilitation System. Institute of Electrical and Electronics Engineers (IEEE) International Conference on Virtual Rehabilitation (ICVR), June 27th—29th, Zurich.
  • Sitzer, D., Twamley, E., & Jeste, D. (2006). Cognitive Training in Alzheimer's Disease: A Meta-Analysis of the Literature. Acta Psychiatrica Scandinavica, 114 (2), 75-90.
  • Soler, A. (2012). ARGUS: Assisting Personal Guidance System for People with Visual Impairment. First Version of the Dissemination Plan. October 30.
  • Tapus, A., Mataric', M. J., & Scassellati, B. (2007). The Grand Challenges in Socially Assistive Robotics. IEEE Robotics and Automation Magazine, 14(1), 35-42.
  • United Nations Department of Economic and Social Affairs—Population Division. (2012). Population Ageing and Development—2012. New York: United Nations.
  • Van Poppel, F., Liefbroer, A., Vermunt, J., & Smeenk, W. (2001). Love, Necessity, and Opportunity: Changing Patterns of Marital Age Hegemony in the Netherlands, 1850-1993. Population Studies: A Journal of Demography, 55 (1), 1-13.
  • Vorst, D. (2012). ARGUS, Assisting Personal Guidance System for People with Visual Impairment. REAL CORP 2012.
  • Wilson, R., Barnes, L., Aggarwal, N., Boyle, P., Hebert, L., Mendes de Lyon, C., & Evans, D. (2010). Cognitive Activity and the Cognitive Morbidity of Alzheimer Disease. Journal of the American Academy of Neurology, 75, 990-996.

The world population will age dramatically by 2050—a problem especially for Japan, Europe and the U.S. Large elderly populations will place a growing strain on human caregivers as well as health and social systems. This case study explores the value added of considering both sex and gender when designing Assistive Technologies for the Elderly.

analyzing how sex and gender interact arrow

Method: Analyzing How Sex and Gender Interact

Assistive technologies support independent living for the elderly. When developing these technologies, it's important to look at sex differences. Women for example live longer, but may have more debilitating disease; men, for example, lose their hearing earlier. In addition, it is important to look at gender differences: as they age, women and men have different partnering patterns (elderly women, for example, more often live alone), men and women have different experience in household management, and elderly men and women have different receptivity to technology. We encourage researchers to analyze how sex and gender interact in individual women and men so that researchers can design the most effective and marketable assistive technologies—designers want their products to be useful and appealing to both women and men.

Gender issues become especially important as assistive technologies become more personalized. Engineers in the U.S., Europe, and Japan are developing robots to help elderly people. Georgia Tech, for example, has created a robotic nurse, named "Cody," that can bathe elderly people. Bathing is an intimate activity that requires careful thought—for women and for men. Carnegie Mellon is developing HERB (Home Exploring Robot Butler) that can fetch household items for you, remind you to take your medicine, or even clean up the kitchen. If there is a robot to clean up the kitchen, I'm ordering it immediately!

As these robots enter our lives, we humans will gender them. Studies of synthetic voices (machine-generated voice) show that human listeners assign gender to machine voices; that is to say, we interpret these machine voices as the voice of a woman or a man, even when the designers may have tried to create a gender-neutral voice (see Making Machines Talk). Apple's Siri (the original iPhone voice) is interesting in this regard. Ask Siri why she is a woman, one of her responses is, "I was not assigned a gender," implying that it's not Apple's fault that you, the listener, ascribe gender to her. As soon as humans interpret a voice as masculine or feminine, we tend to apply all of our cultural stereotypes to the machine.

Gendered Innovations:

Considering sex and gender when designing new assistive technologies will be ONE important factor to ensure that the products are successful with all users.

  1. Assessing women's and men's needs for assistive technologies.
  2. Developing assistive technologies considering women's and men's needs.
  3. Using participatory design to create the next generation of assistive technology.