The need to identify pain in patients with dementia

There are an estimated 35.6 million people with dementia worldwide and this figure continues to rise. Treatment and care often fall below basic standards due to the unique and complex challenges presented by dementia. Identification and assessment of pain in people with dementia present a particular challenge [1]. It is thought that up to 80% of people with dementia living in care homes regularly experience pain from different causes [2]. The exact prevalence of pain is still difficult to determine because there are biasing factors like the beliefs of the caregivers how much pain is present in patients with dementia [3, 4] and the tendency of under-reporting pain in the group of patients with verbal communication problems [5]. Against this background, it is not surprising that epidemiological research has reported that the use of pain medication is often inappropriate in this patient group [6]. This is particularly prominent in care home and hospital settings where people are likely to have more severe cognitive impairment and are reliant on administration of analgesics by health professionals. A large number of studies have emphasized the challenge of assessing pain in people with dementia in these settings, and it is likely that this is the primary contributing factor to under-treatment of pain in these individuals [6].

Accordingly, thorough assessment of pain is essential to ensure effective treatment and ongoing care. In most patient groups the most effective method of identifying pain is through self-report. However, at moderate or severe stages of dementia, people with dementia often lack insight into their condition. In addition, a key symptom of dementia is the loss of ability to communicate, particularly in the later stages of the condition. These factors combined mean that people with dementia might not have the ability to give an accurate report of their pain [7]. As a result the majority of the usual pain assessment tools are partially inappropriate for use in dementia. Thus, a key element of any comprehensive assessment tool for dementia would be the observation of pain-related behaviors as necessary substitute for verbal report of pain, especially in moderate and severe dementia [8].

The human observer of pain as substitute of the self-report of pain: promises and challenges

As mentioned above, research suggests that self-report seem to be valid in patients with mild degrees of dementia. However, self-report in persons with a score below 18 on the Mini Mental Status Examination (MMSE), indicative of moderate-to-strong dementia, may not be valid [9]. Thus, when dementia progresses and the person becomes less verbally communicative, observational pain tools become more important. Behavioral pain assessment tools typically focus on direct observation of pain-related behaviors, i.e. facial expression, body posture and movement, vocalization, etc., and also may include changes in behavior and functioning [8]. Research on pain in persons with dementia has largely been dominated by studies on the development of pain assessment tools, with a tremendous increase in attempts during the last two decades. The impressive number of development attempts shows the urgent need of appropriate tools and the insight that most tools are still far from perfect. But what are the promises and challenges so far?

A road map to find a better solution by use of a new interdisciplinary developed video-based system

One way to overcome some of the above mentioned challenges, and thus, improve pain assessment in patients with dementia, is to make use of an automatic video-based pain detection system as a complementary instrument supporting the human caregiver [7]. Such attempts are never meant to substitute the human observer or caregiver. Similarly as other monitors of vital functions (e.g. blood pressure, heart rate, respiration), a video-based pain monitor should unburden nurses and caregivers and thus, give them more time for the psychosocial and empathic side of human care.

Although automatic pain monitoring has also used modalities other than facial expressions, like physiological signals such as skin conductance, pupil dilatation and electrocardiogram (ECG) [23], the facial expression promises better discriminative validity and thus, better pain specificity compared to these other modalities, which mostly allow to indicate the level of arousal but not the specific type of distress. For that reason, we will focus on the detection of facial expression of pain.

The following sections of the manuscript will elaborate a kind of road map how to reach this goal to develop a video-based pain monitor focusing on the facial expression. One reason for writing a road map paper is to inform colleagues about our developmental strategies, technical solutions and adaptation to the needs of pain care in patients with dementia. By that, we would like to encourage them to join forces. This is to avoid unnecessarily scattered attempts of solution as was hampering the development of an internationally agreed on observational pain scale. Another reason for this road map paper is to inform the end-users, such as nurses and caregivers, about what might technically be possible in the future. They have to finally evaluate the feasibility of such automatic pain detection systems and should have visions of the potential optimization of pain care of patients with dementia.

In order to develop a system that is capable of identifying pain in patients with dementia, it is crucial to apply an interdisciplinary approach that comprises expertise in basic as well as applied research in the fields of pain, communication, dementia, facial expression, video image analyses, data analyses, machine learning as well as in clinical aspects of pain assessment in patients with dementia. In the following, we will describe our interdisciplinary approach and compare it to previous approaches.

The technical system for automatic detection of facial expressions envisaged in this article outputs Action Unit intensities based on the Facial Action Coding System. On the other side, the authors of this article from the psychology domain have in the past significantly contributed to the establishment of facial expression of pain data sets with cognitively healthy as well as impaired subjects. These data sets have been manually annotated with FACS Action Units and significant contributions to the understanding of facial expression of pain have been made, see e.g. [14]. In our joint preliminary work, based on this data, we already successfully qualitatively tested the feasibility of pain recognition in cognitively impaired elders. The complementary competencies, the availability of suitable data sources and the common language given by the Facial Action Coding System promote our belief that we can leverage automatic pain recognition in the cognitively impaired following our joint roadmap.

Technical steps

In the last 20 years, major advances have been achieved in computer vision research for automatic recognition of facial expressions, with necessary and good progress in different areas of this field. Three of these areas have been targets of further improvement for developing an automatic pain detection system by the engineers of our interdisciplinary group. These are: (i) capturing the face, (ii) analyzing facial motions and (iii) applying knowledge-level diagnosis of pain. Figure 1 tries to give a systematic overview over these three areas, with the different colored boxes (black, light grey and dark grey) representing the three areas.

• Step of robust face capturing (black colored boxes of Fig. 1)

  We use the “Sophisticated High Speed Object Recognition Engine” (SHORE, [40, 41]) developed by Fraunhofer IIS for detection of faces. Frontal face detection rate of the SHORE system is 91.5% with 10 false positives when tested on the public CMU + MIT data set (http://vasc.ri.cmu.edu/idb/html/face/frontal_images/index.html). This data set contains 507 annotated faces in 130 grayscale images. The face detection is based on local census and structure features. For classification, a classifier cascade is used (for more details see [40]) and together with a coarse-to-fine grid search this leads to an efficient real-time face detector. SHORE is also able to detect four basic emotions (anger, happiness, surprise, sadness) as well as valence (hedonic tone of the feeling (positive vs. negative)) [42]. Within our framework the SHORE system is used to locate the person’s face as well as the position of eyes, nose and mouth corners in each image of the video stream. The face is then normalized with respect to rotation and scaling. Thus, the normalized image always has the same resolution and pose. In this way, at least some of the variations in the appearance of the face that are caused by head rotations and movements of the person in front of the video capture device are mitigated, making this approach robust enough for capturing faces of bedridden patients. If more than one face is present, then the face detector selects the most prominent face on the basis of the face size in the image. If no face is detected, then the frame is not processed further.

• Step of analyzing single facial motions (light grey colored boxes of Fig. 1)

  Automatic detection of pain and pain levels from facial expressions is generally performed as a single or two-level detection process. In the former case, image sequences are processed directly when sequences can be supposed to be indicative of pain to extract characteristic features (e.g. [24, 31, 35]). In the latter case, image sequences are first processed for detecting single facial motions and coding them in terms of FACS (namely as AUs). Then (in a second step), the detected AUs and their intensities are processed to determine the likelihood of the presence and intensity of pain according to some thumb rules based on the available literature [26, 34]).

  Color or grayscale image sequences are commonly used as input to pain detection systems [24, 26, 30, 35]. More recently, depth and thermal images are also being used in combination with color images [43]. Numerical features describing the geometric shape or textural appearance of the face are extracted from each image in a sequence. The shape and appearance features are often used in combination [30, 31]. To incorporate expression dynamics, features are extracted over multiple images within a certain time interval [35]. In the two-level pain detection process, temporal features are also extracted from AU intensities [26]. The extracted features are processed using various machine learning methods in order to detect pain.

  In contrast to other approaches, our AU detection implements a temporal state model that connects each frame to the next [44]. This leads to a logical connection between successive frames, and with this property the system is able to mitigate noise and effectuate a temporal smoothing of the output. It is worth noting that the visco-elastic properties of facial muscles are taken into account in our state model by an individual mass-spring-damper model per AU. For the detection of the intensity of AUs in each frame, two sources of information are used.¹ The geometric displacement of key points of the face (e.g. mouth corners) and texture information (e.g. wrinkles) are fused within the framework to make a final decision on the intensities of a selected set of AUs. During this process, an internal model of the facial morphology of the person is also taken into account. This model of the person’s “neutral” face is determined over time and helps to calibrate the system to the person’s face at runtime automatically [44]. This online calibration is necessary because it is often not possible to acquire a neutral face on demand. So in comparison to other approaches, we do not rely on an explicit calibration phase using a static mean face as a neutral face, since we think that this is not precise enough and will cause problems in distinguishing subtle expression related changes in the face from calibration errors.

• Step of applying knowledge-level diagnosis of pain (dark grey boxes of Fig. 1)

  Based on the identification of the temporal sequence of AUs and their intensities, a knowledge-level model can be built for diagnosis (see Fig. 1) – that is, the decision whether a patient experiences pain during the present video segment. Input in such a model is a pattern of AUs and output is the diagnosis. The diagnosis is performed by means of the application of symbolic rules, which represent patterns of AUs which are indicative for pain. Because the rules are represented symbolically, the diagnostic decision can be explained to a human observer.

  Diagnosis can be based either on prototypical, group-specific or individual patterns of AUs. Although a distinctive pain-indicative set of prototypical facial muscle movements has been identified that is displayed universally during pain [45], there are also substantial variations between individuals. We recently demonstrated that facial expressions of pain are best described as four distinct facial activity patterns of pain, shown reliably by certain groups of individuals, rather than as one single prototypical set of movements [46]. The most stable and most frequent patterns were ‘narrowed eyes’ combined with either (I) ‘wrinkled nose’ and ‘furrowed brows’; (II) ‘furrowed brows’ or (III) ‘opened mouth’ (the fourth pattern was not stable enough for further consideration). We could show that the most prominent facial movement which is part of each facial activity pattern, namely the ‘narrowed eyes’ encodes the sensory dimension of pain, whereas ‘wrinkled nose’ and ‘furrowed brows’ encode the affective dimension of pain [16]. Given these findings, the knowledge-based model will consider these three distinct facial activity patterns as well as consider whether a facial response might be indicative of pain intensity or pain affect in the diagnosis process. We hope that incorporating this knowledge in the automatic diagnosis process will improve sensitivity and specificity. By analogy, human observers benefit in their recognition aperformance from becoming aware of the presence of different facial activity patterns indicative of pain [47].

  The knowledge-based model is constructed either by classifier learning or by unsupervised learning (e.g. [48]). In the first case, a training set needs to include AU sequences observed for pain episodes as well as for non-pain episodes (e.g., disgust), the classifier is trained such that the rules have high sensitivity as well as high specificity for pain [49, 50]. In the second case, only pain episodes characteristic patterns are identified [50]. To exploit as much information as possible from the observed AU sequences, a rich representation language which also allows including domain specific knowledge as a background theory is helpful. Therefore, we currently investigate the application of inductive logic programming (ILP [51]) to learn diagnostic rules. In this framework, it is possible to learn rules which either only include information about the presence and possibly the intensity of specific AUs or rules which take into account information about sequences and simultaneous occurrence of AUs. A first empirical investigation indicates that human observers take sequential information into account [52]. At later stages of the process, we intend to use knowledge level diagnosis of pain that can be extended to sub-group classification learning. For example, knowing that facial expressiveness to pain is increased in patients with Alzheimer’s disease (AD) [14] and reduced in patients who are suffering from Parkinson Disease (PD) [20], we will at later stages apply sub-group classification learning (separately for patients with AD and PD), to possibly account for these pathological alterations.

Testing the feasibility of our system in care settings of elders with dementia

There are mainly two stages of development. Stage 1 includes sampling of videos during care in situations of daily living with spontaneous or guided movements, which likely produce pain. This stage may be accompanied by video recordings during assessment of pressure pain sensitivity (e.g. simple palpation). The recording will be done by a camera man, who has to ensure ideal recording conditions. Since this approach is very time and staff consuming, in stage 2, a living lab with an appropriate multi-camera system will be used, in which senior home residents spend part of the day. Such a system provides a broader data base because - besides pain episodes provoked by a caregiver through guided movement -, spontaneous pain can no longer be missed due to continuous recording. Later on, night recording by ultraviolet cameras might be envisioned. The videos will be examined first offline. However, should move more and more towards the time constraints of online analyses.

Examples of future use and implementation

One of the first applications of our envisaged video system can be expected in patients with dementia who are immobile and are lying in bed. Given the technical solutions available at the moment, the reliable capture of the face will be possible only when the range of motions of the face is limited. Patients lying in bed can be expected to present enough facial aspects in frontal and lateral views to allow our video systems detecting the relevant facial expressions of pain. Is there need for such limited applications?

Unfortunately, the end-of-life constitutes a phase when pain often tremendously reduces the quality of life and hereby prevents dignified dying [53], because under-treated pain causes unnecessary suffering whereas over-treated pain (too much analgesics) cause unnecessary sedation. These problems are augmented in patients with dementia because they cannot report about pain and thus, make it more challenging to titrate the best possible dosage of analgesics [53]. In this palliative phase, there is definitely urgent need for a support that helps caregivers to decide about the appropriate dosage of medication for patients.

Another example of adequate application of our video system is the postoperative phase of pain monitoring. Dementia does not protect from physical causes of surgery (e.g. hip fractures). The sufficient management of acute postoperative pain is nowadays possible in many cases to avoid undue suffering and development of chronic postoperative pain. However, this adequate pain management requires the active assistance of the vigilant patient. The advantage of patient-controlled analgesia (PCA), when the patient controls behaviorally the dosage of analgesics, has been well documented [54]. However, PCA requires the patients to be active and vigilant (to decide which dosage of analgesics is sufficient) and thus, is mainly possible in cognitively unimpaired patients. Given the possibilities of our video system, this limitation might no longer apply, because this system might also help to titrate the necessary dosage. The urgent need for such systems might be further demonstrated by the fact that elderly and especially cognitively impaired patients are more likely to become delirious in the postoperative phase due to surgery or the aftereffects of anesthesia, preventing their active contribution to dosage finding. Also in these cases, our video-system may be excellent support of decision making as regards the appropriate pain management because the immobile and supine delirious patient provides best prerequisite for its application.

The scope of application will be widened as soon as more active systems including maneuverable swivel arms become available which allow targeting the face of more mobile patients over a wide range of motions.