The Impact of Wearables in Improving Silver Economy


The silver economy is referred to as the opportunities that arise from the business models, social services (policies), and consumer expenditure connected to population aging and the aging-related needs of the elderly beyond the age of 50. The silver economy relies on the fact that the aging population is based on the imperative that they understand, adapt, and innovate the new technology. According to the World Economic Forum: “Ageing must be reframed so that this time of life is one of productivity and prosperity”. As elderly people, population growth is irreversible and irrefutable and hence it creates a remarkable turn to boost the silver economy. According to the silver economy framework of elderly and ambient assisted living are counted as producers, investors, and consumers in the community. According to [1] in between 2015 and 2060 the number of people aged 65 years or over in Europe and the U.S. will be almost double, which will eventually highlight the need of bringing continuous healthcare support and medical assistance. This need becomes even more critical if that age group belongs to groups exhibiting some chronic or neurocognitive disorder. Apart from this, older people desire to continue their independence as long as possible, trying to fend for themselves by having a fulfilling life in terms of everyday activities and leisure. Hence wearables and services for Ambient Assisted Living (AAL) stand out as a possible solution to address these challenges.

A few factors will discourage the elders from the use of technology such as technology anxiety, resistance to change, and need to knowledge self-actualization and privacy aspects [2].

The study below shows how wearable technology can boost the silver economy and how it will continue to have a profound impact on economic development


The wearable market is still fast developing and expected to grow exponentially in the oncoming years. The wearable market was valued at $70bn in 2019 [3].

Moreover, the wearable health technology (WHT) global market deserves special attention. The increased rate of income was predicted as 46,6 % in the years 2015-2020 according to the data in 2017. The WHT global market is expected to reach 1630.3 million dollars by 2020 based on the report prepared by P&S Market [4]. However, the WHT is not a standard market, it combines the requirements of the technology and healthcare market. The studies showed that the acceptance of healthcare wearable technology is dependent on technical, health, and consumer attributes [4]. One of the target markets of the WHT is the senior group because of the most frequently occurring chronic and neurodegenerative diseases [5].


Wearable technology is an approach that offers a viable means of ubiquitous, sustainable, and scalable monitoring in a free-living environment [6]. Commercial wearables are proven to provide accurate analytics that is developed using heterogeneous wearable sensors i.e. accelerometers, gyroscopes, force or pressure sensors, photoplethysmography (PPG), skin sensors, etc. with a combination of algorithms for outcomes that serve to improve quality of life (QoL). In the table, we dig deeper into these available devices (Tab. 1.).

Tab. 1. Application of wearable devices.

Ref Functionality measured the by wearable device Form Factor Hardware Components Application scenario
[8] Motor Activities Attached on the chest, over the wrist on the dominant arm and on the dominant side’s ankle, respectively), Physical posture detection sensors basically inertial measurement units (IMU) Improve overall well-being; i.e. physiological and psychological health
[9] Motor disability and remote monitoring system Left-under arm Wearable Body Sensor System (WBS) includes a Smart Garment with mobility sensors for respiratory rate, skin temperature, heart rate and includes a fall and activity monitoring sensor and Electronic Control Unit (ECU) To detect emergency situations, such as falls, in order to promote independent living
[10] Sleep (Daily activity) Located at head and sides of bed Three PIR motion sensors Monitor sleep status
[11] Abnormal motor activity detection Waist and brain Wireless accelerometer and electroencephalograph (EEG) logger integrated in our minimally invasive monitoring sensor (MIMS) system Fall monitoring system for Alzheimer’s disease
[12] Healthcare monitoring system Arms Blood pressure, ECG (electrocardiogram), body temperature and other medical data from sensors ‘Big Data-based Precision Medicine Cloud Platform continuous testing of the user’s physiological parameters, and further detection
[13] Track physical assault Wrist (1) Sensors; (2) Analyze and classifier unit; (3) Communication module; (4) Native display module; and (5) a Phone-based App Agent Smart bracelet for personal safety
[14] Vision Eyes MedGlasses system consists of a pair of wearable smart glasses, an artificial intelligence (AI)-based intelligent drug pill recognition box, a mobile device app, and a cloud-based information management platform MedGlasses: A Wearable Smart-Glasses-Based Drug Pill Recognition System Using Deep Learning for Visually Impaired Chronic Patients

Hearing problem

Mounted on the person in the outdoor and external environment Raspberry-pi, grove sensors, microphone and vibration motor Real-Time Detection of Important Sounds with a Wearable Vibration Based Device for Hearing-Impaired People


[16] Improve gait impairment (rehabilitation device) Feet Laser and visual cueing system Visual cueing using laser shoes reduces freezing of gait in Parkinson patients both in the laboratory and at home



Both market penetration and literature research prototypes of IoT wearable sensors and devices have grown a lot over the past decade, with applications in many aspects of lifestyle and healthcare, including the elderly demographic. However, not only the development of devices and sensors influence the silver economy, but also the progress in technologies standing behind them. Among others, the development in the field of machine learning allows developers to create increasingly more robust and accurate solutions for monitoring vital signs, localization purposes and recognizing physical activities of elders [17], [18]. However, the success of wearable devices in the market dedicated to seniors is more complex and depends on socio-technical characteristics and aging-related factors such as effort expectancy, technology anxiety, performance expectancy, resistance to change [19]. Additionally, the outfit of the wearable technology should be considered by the designers. This factor is not so obvious, however, the elders wish not to feel the stigma of old age limitations. For this reason, the devices should be as transparent and invisible as possible. This feature will also be a driving factor in the development of the novelty at the wearable market likewise will increase demand for such technologies [20]. Generally, the need for accustoming the device features should be focused on the difference in the cognitive and motor abilities of seniors versus the rest part of the age pyramid of the society [20].  According to the functionality, the most expected utility of the wearables by the elders are signaling during emergencies and monitoring vital signs [21].


This blog entry is focused on one of the most exciting phenomena of today that are the role of wearables in boosting the silver economy. We have analyzed the significance of the silver economy. The aging society is creating the space for the blowing of the wearable market targeted for the seniors. We have indicated what kind of factors have influenced the consumer behaviour in this group.


[1] European Union, The 2015 Ageing Report. Underlying Assumptions and Pro-Technologies and Applications for Active and Assisted Living. Current situation.

[2] Kolasinska, Agnieszka, et al. “Technology and aging: Users’ preferences in wearable sensor networks.” Proceedings of the 4th EAI International Conference on Smart Objects and Technologies for Social Good. 2018.

[3] J. Hayward, Wearable Sensors 2018-2028: Technologies, Markets & Players. IDTechEx Ltd., 2018.

[4] Zhang, Min, et al. “Technical attributes, health attribute, consumer attributes and their roles in adoption intention of healthcare wearable technology.” International journal of medical informatics 108 (2017): 97-109.

[5] Li, He, et al. “Examining individuals’ adoption of healthcare wearable devices: An empirical study from privacy calculus perspective.” International journal of medical informatics 88 (2016): 8-17.

[6] Godfrey, A., 2017. Wearables for independent living in older adults: Gait and falls. Maturitas, 100, pp.16-26.

[7] Lee, J., Kim, D., Ryoo, H.Y. and Shin, B.S., 2016. Sustainable wearables: Wearable technology for enhancing the quality of human life. Sustainability, 8(5), p.466.

[8] Bleser, G., Steffen, D., Reiss, A., Weber, M., Hendeby, G. and Fradet, L., 2015. Personalized physical activity monitoring using wearable sensors. In Smart health (pp. 99-124). Springer, Cham.

[9] Bourke, A.K., Prescher, S., Koehler, F., Cionca, V., Tavares, C., Gomis, S., Garcia, V. and Nelson, J., 2012, August. Embedded fall and activity monitoring for a wearable ambient assisted living solution for older adults. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 248-251). IEEE.

[10] Zeng, Y.C. and Chang, W.T., 2016, October. Estimation of sleep status based on wearable free device for elderly care. In 2016 IEEE 5th Global Conference on Consumer Electronics (pp. 1-4). IEEE.

[11] Abbate, S., Avvenuti, M. and Light, J., 2014. Usability study of a wireless monitoring system among Alzheimer’s disease elderly population. International journal of telemedicine and applications, 2014.

[12] Liu, Y., Zhang, L., Yang, Y., Zhou, L., Ren, L., Wang, F., Liu, R., Pang, Z. and Deen, M.J., 2019. A novel cloud-based framework for the elderly healthcare services using digital twin. IEEE Access, 7, pp.49088-49101.

[13] Patel, J. and Hasan, R., 2018, January. Smart bracelets: Towards automating personal safety using wearable smart jewelry. In 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC) (pp. 1-2). IEEE.

[14] Chang, W.J., Chen, L.B., Hsu, C.H., Chen, J.H., Yang, T.C. and Lin, C.P., 2020. MedGlasses: A Wearable Smart-Glasses-Based Drug Pill Recognition System Using Deep Learning for Visually Impaired Chronic Patients. IEEE Access, 8, pp.17013-17024.

[15] Yağanoğlu, M. and Köse, C., 2018. Real-Time Detection of Important Sounds with a Wearable Vibration Based Device for Hearing-Impaired People. Electronics, 7(4), p.50.

[16] Barthel, C., Nonnekes, J., van Helvert, M., Haan, R., Janssen, A.M., Delval, A., de Vries, N.M., Weerdesteyn, V. and Debû, B., 2018, June. Visual cueing using laser shoes reduces freezing of gait in Parkinson patients both in the laboratory and at home. In 4th International Workshop on Freezing of Gait (p. 39).

[17] Baig, Mirza Mansoor, et al. “A systematic review of wearable sensors and IoT-based monitoring applications for older adults–a focus on ageing population and independent living.” Journal of medical systems 43.8 (2019): 233.

[18] Wang, Zhihua, Zhaochu Yang, and Tao Dong. “A review of wearable technologies for elderly care that can accurately track indoor position, recognize physical activities and monitor vital signs in real time.” Sensors 17.2 (2017): 341.

[19] Talukder, Md Shamim, et al. “Predicting antecedents of wearable healthcare technology acceptance by elderly: A combined SEM-Neural Network approach.” Technological Forecasting and Social Change 150 (2020): 119793.

[20] Lewis, Joanna E., and Mark B. Neider. “Designing wearable technology for an aging population.” Ergonomics in Design 25.3 (2017): 4-10.

[21] Zhang, Bo, and Yan Li. “Wearable medical devices acceptance and expectance of senior population in China.” Proceedings of 17th International Conference on Electronic Business. 2017.