Flex Sensors: Possibility of Detecting Improper Posture of a Runner’s Arm

According to sports coaches, the upper body posture is an important factor in running. This paper shows it is possible to detect an improper posture of a runner’s arm using a flex sensor. It does this by showing how accurately a flex sensor can describe an angle. It also shows in which location on the arm the sensor should be placed. Lastly, it is shown how the sensor performed in actual running exercises, although the accuracy during these runs was not calculated due to limited resources and time

Utilising Emotion Monitoring for Developing Music Interventions for People with Dementia:A State-of-the-Art Review

The demand for smart solutions to support people with dementia (PwD) is increasing. These solutions are expected to assist PwD with their emotional, physical, and social well-being. At the moment, state-of-the-art works allow for the monitoring of physical well-being; however, not much attention is delineated for monitoring the emotional and social well-being of PwD. Research on emotion monitoring can be combined with research on the effects of music on PwD given its promising effects. More specifically, knowledge of the emotional state allows for music intervention to alleviate negative emotions by eliciting positive emotions in PwD. In this direction, the paper conducts a state-of-the-art review on two aspects: (i) the effect of music on PwD and (ii) both wearable and non-wearable sensing systems for emotional state monitoring. After outlining the application of musical interventions for PwD, including emotion monitoring sensors and algorithms, multiple challenges are identified. The main findings include a need for rigorous research approaches for the development of adaptable solutions that can tackle dynamic changes caused by the diminishing cognitive abilities of PwD with a focus on privacy and adoption aspects. By addressing these requirements, advancements can be made in harnessing music and emotion monitoring for PwD, thereby facilitating the creation of more resilient and scalable solutions to aid caregivers and PwD

Channel state information (WiFi traces) for 6 activities

Channel state information collected for 6 activities (sitting, clapping, waving, jumping, falling, walking) for 9 unique participants over a total of 6 days. The first 3 days are unique participants, last 3 days same participants. Fs = 50 H

Dataset: Channel State Information for Different Activities, Participants and Days

In our current society, unobtrusive sensing has become an important tool to monitor the physical world, as it is easy to use and privacy-aware. Remote sensing is a new and heavily researched technology based on the analysis of radio signals. A particular field research in this area is the analysis of channel state information with the raw signal, as this contains the most information. While most research focuses on analysis of individuals or clustered data, little to no research has gone into the analysis of channel state information of multiple people over multiple days for different and comparable activities. This dataset contains data of nine different participants over three different days, with an two participants repeating the activities over an additional three days. The dataset is available at the 4TU.ResearchData under the CC BY-NC-SA license

Scaling Activity Recognition Using Channel State Information Through Convolutional Neural Networks and Transfer Learning

Unobtrusive sensing is receiving much attention in recent years, as it is less obtrusive and more privacy-aware compared to other monitoring technologies. Human activity recognition is one of the fields in which unobtrusive sensing is heavily researched„ as this is especially important in health care. In this regard, investigating WiFi signals, and more specifically 802.11n channel state information, is one of the more prominent research fields. However, there is a challenge in scaling it up. Transfer learning is rarely applied, and when applied, it is done on filtered/modified data or extracted features. This paper focuses on two aspects. First, convolutional networks are used across multiple participants, days and activities and analysis is done based on these results. Secondly, it looks into the possibility of applying transfer learning based on raw channel state information over multiple participants and activities over multiple days. Results show channel state information is accurate for single participants (F1-score of 0.90), but sensitive to different participants and fluctuating WiFi signals over days (F1-score of 0.25-0.35). Furthermore, results show both clustering and transfer learning can be applied to increase the performance to 0.80 when using minimal resources and retraining

Channel State Information for Human Activity Recognition with Low Sampling Rates

In this paper, it is shown that lower transmission/sampling rates can be used in human activity recognition using channel state information (F1-scores > 85%) and that extremely high sampling rates are unnecessary once the system has been deployed. This is done by analysing the effects of interpolating different sampling rates on Wi-Fi dynamic channel state information for human activity recognition. While current research focuses on training and testing with homogeneous and very high sampling rates (> 100 Hz), this paper outlines some issues with higher sampling rates and explores the impact of training and testing with heterogeneous sampling rates in order to advance more towards joint communication and sensing, where one cannot be certain of the received data rate over time while not knowing the exact training set due to weight sharing in Federated Learning. This paper shows the effect of training and testing with heterogeneous sampling rates (including interpolated datasets) on convolutional neural networks in WiFi sensing

Joint Communication and Sensing for Human Activity Recognition in Wi-Fi 6E

Health care and human comfort are intertwined, but proper health care is costly, especially in the case of dementia. Device-free sensing has the potential to increase safety and comfort in activities of daily living in human lives by enabling truly unobtrusive ways to achieve human activity recognition. However, in order to truly enable device-free sensing in real-life scenarios, it should be integrated seamlessly in wireless communication. Currently, transmitting devices employ high (> 100 Hz) constant transmission rates and the designed neural networks cannot deal with realistic wireless networking, including changing receiver rates

Personal Hygiene Monitoring Under the Shower Using WiFi Channel State Information

Personal hygiene is often used to measure functional independence, which is how much support someone requires to perform self-care. By extension, this is often used in the monitoring of (early-stage) dementia. Current technologies are based on either audiovisual or wearable technologies, both of which have practical limitations. The use of (NLOS) radio-frequency based human activity recognition could provide solutions here. This paper leverages the 802.11n channel state information to monitor different shower-related activities (e.g. washing head or body, brushing teeth, and dressing up) and the degree to which some of these can be monitored, as well estimating different water pressures used while showering for multiple locations in the apartment. Wavelet denoising is applied for filtering and a convolutional neural network is implemented for classification. Results imply that for coarse-grained activity recognition, an 퐹1-score of 0.85 is achievable for certain classes, while for fine-grained this drops to 0.75. Water pressure estimation ranges from 0.75 to 0.85 between fine-grained and coarse-grained, respectively. Overall, this paper shows that channel state information can be successfully employed to monitor variations in different shower activities, as well as successfully estimating the water pressure in the shower