Barriers and Guidelines to Adopting Wearables in Sports
Athletes can rely on sensors to assess performance, but device adoption comes with technical and compliance challenges.
The sports wearable landscape
With the speed of a sprinter and the strength of a boxer, innovations are impacting every facet of the sports business. According to a survey by PwC, 69.7% of the respondents mentioned that the technologies augmenting physical activities represent a key market force in the industry. Furthermore, innovations positively impact both the athlete and fan experience.
One of the major tech trends in this regard, however, is the rising adoption of sports wearables in combination with health or fitness apps, as reported by McKinsey (2021). As the name suggests, these devices can be worn by amateurs or professional players to track relevant metrics, facilitating training and ensuring athlete welfare.
Studies prove the superiority of a data-driven approach to athlete training unlocked by sports wearables compared to traditional coaching. In particular, the National Center for Biotechnology Information reported that wearable-based vibratory feedback helped to increase the swimming stroke rate. The wearable technology in sports can also facilitate the assessment of athletes’ psychological state. For instance, the universities of Lapland and Istanbul delivered a machine learning–based system, helping coaches to assess the stress level of players with 85% accuracy.
As you can understand from the example above, sports wearables are just the tip of the iceberg. We may see them as the eyes and ears of complex systems that encompass multiple technologies, such as the Internet of Things (IoT) and machine learning (ML). Deloitte describes a multilayered architecture of such platforms as follows:
- Sensors integrated into athletes’ clothing and accessories to collect biometric data
- A network layer to transmit this data via gateways and communication protocols
- An integration layer to aggregate and store information, including an IoT middleware and a data storage
- A data analytics module to process information and turn it into actionable insight
- Dashboards to visualize the insights in an intuitive format
For more technical details and use cases, read our blog post:
