| | 7 | == Problem in Depth == |
| | 8 | |
| | 9 | As people's lives become sedentary, they are at increased risk for chronic illnesses such as obesity and heart disease. As a result, the desire for maintaining good health has increased. One of the most important and efficient ways of becoming and maintaining good health is through good exercise. However, it is hard for people to dedicate necessary time to perform exercises during the day. Thus, more people are not exercising in dedicated exercise places, and instead exercising at their home or office. |
| | 10 | |
| | 11 | There are previous solutions to monitoring exercises at home, but all have drawbacks which leave more to be desired. Personal trainers are beneficial to monitoring exercise form, but have many complications. Trainers can have multiple clients, and thus a schedule that an exerciser might not be able to adhere to on a week-by-week basis. Trainers are also expensive, averaging about $55 for an hour session in 2019. Group fitness training is a little cheaper, at $35 a class, but that is still expensive for some consumers. Fit-bits and smartphones also have exercise monitoring software, such as heart rate monitors and step counters. However, these tools are more useful for endurance exercises. Additionally, these devices cannot measure exercise form, which may lead the user to having an inefficient work out or suffer a personal injury. Recently, smart sensor suites have been developed which can monitor user's exercises. Unfortunately, these suites usually require people to attach sensors to themselves and their exercise equipment, which is uncomfortable, unnatural, and expensive. |
| | 12 | |
| | 13 | == Solution in Depth == |
| | 14 | |
| | 15 | Our solution uses the channel state information (CSI) that is embedded in !WiFi signals to determine what exercise a user is performing. !WiFi is the best candidate because it is ubiquitous in a most settings, meaning users would not have to buy dedicated devices for exercise monitoring. Additionally, individual features can be derived from CSI, allowing the detection of different exercises. To extract these features, we implemented a |
| | 16 | |
| | 17 | ADD TYPE OF DEEP LEARNING MODEL HERE |
| | 18 | |
| | 19 | that searches for repetitive patterns in the CSI data using autocorrelation, which compares a signal to a copy of itself at a different point in time. |
| | 20 | |
| 9 | | We used a TP-LINK router with 2.4GHz and 5GHz frequencies. We used a Dell Laptop with an Ubuntu 14.02 kernel and an Intel !WiFi Wireless Link 5300 MIMO radio, also known as the IWL5300. We used the Linux 802.11n CSI tool (created by Daniel Halperin, Wenjun Hu, Anmol Sheth, and David Wetherall), implemented in Matlab and C, to extract the CSI from the channel measurements. For the two-layer deep neural network, we used Python and Tensorflow. |
| | 23 | We used a TP-LINK router at the 5GHz frequencies. To receive the CSI data from !WiFi, we used a Dell Laptop with an Ubuntu 14.02 kernal and an Intel !Wifi Wireless Link 5300 MIMO radio (IWL5300). We used the Linux 802.11n CSI Tool (created by Daniel Halperin, Wenjun Hu, Anmol Sheth, and David Wetherall), which is built off of the IWL5300. This tool allowed us to collect and read CSI data on the client computer. |
