Human Fall Detection in Indoor Environments Using Channel State Information of Wi-Fi Signals

The aim of this project is to detect the fall of an individual in indoor environments by monitoring Wi-Fi channel state information (CSI) including amplitude and phase and using this data to recognize the person’s activity. In an indoor setting, where Wi-Fi access points are present, human movements change the propagation characteristics of the environment, thereby altering the effective channel and the CSI of the received signal. Under some conditions, we can recognize this change in CSI characteristics as a signature of the activity performed and thus, identify it. Our experimental setup consists of two Wi-Fi access points between which a subject performs various activities. We use an augmented PCA technique to extract CSI features and apply both traditional supervised (SVM, Decision Trees, Multinomial Logit Regression) and deep (Long Short-Term Memory) learning methods to our data. LSTM achieves the best accuracy in our validation dataset, approximately 80%. Introduction & Related Work Many individuals, especially elderly people, live alone despite facing, potentially undiagnosed, health problems. In such circumstances, a fall can indicate an immediate threat to their wellbeing and even, survival. Therefore, detecting falls as early and accurately as possible is critical to treating them in a timely and effective manner. Several, currently implemented, systems rely on the individual pressing an emergency button or alternatively, on designated professionals checking on the individual at regular intervals. However, when an individual falls, they may already be unconscious, in which case the former fails. Additionally, in the case of a heart attack or severe stroke, the individual needs to be treated within a few minutes, which means that the latter method is either unsuccessful or highly impractical. Interesting proposed solutions involve using wearable sensors measuring acceleration ([5]), radars ([6]) or cameras ([7]). Nevertheless, elderly people often complain about having to carry a sensor around, reasonably priced radars may have very limited range of operation on the order of decimeters ([6]) and cameras require line of sight and sufficient lighting to operate effectively, while also raising privacy concerns. This is where fall detection based on Wi-Fi comes into play. It is unobtrusive, respects the individual’s privacy, works any time of the day and only requires equipment regularly found in homes today, namely Wi-Fi access points (APs). Human bodies reflect and scatter Wi-Fi signals and thus, movements affect the channel between the transmitter and the receiver leaving distinguishing signatures in the CSI data. Hence, activity recognition and fall detection are possible. Few papers have attempted to perform activity recognition using Wi-Fi CSI data. First, [8] recognizes spoken words based on the CSI variation caused by lip movement, but its main shortcoming is that it relies on directional antennas to lower the signal noise, whereas most commercially available APs have omni-directional ones. Then, [9] uses CSI histograms to identify activities, such as taking a shower or washing dishes. However, the activities recognized are location dependent, whereas falls are not and thus, the methods applied do not carry over to our task. Moreover, utilizing only the CSI histogram removes a lot of information from the dataset. Moving closer to our goal, [1] performs relevant activity recognition using a model-based approach, in which CSI data are correlated with movement speeds and speeds are then correlated with activities. Hidden Markov Models are used to compute the parameters of these models and the accuracy achieved is exceptional, greater than 95%, but we are interested in model-free methods. The closest approach to ours is described in [2] and specializes in fall detection using Wi-Fi CSI data of a 3x3 MIMO channel. Anomaly detection, first, detects the start of an activity and an SVM recognizes falls among those activities achieving an accuracy greater than 85%. We try to investigate how different learning algorithms perform at this task without utilizing anomaly detection and by